Dedicated AI engineering teams · Enterprise delivery · NVIDIA-certified

AI Consulting Services From Engineers Who Build — Not Consultants Who Only Advise

Build a dedicated AI engineering team in 14 days. Computer vision, generative AI, MLOps, edge AI, and AI agent specialists, pre-vetted, ISO 27001-aligned, with full IP transfer from day one. 70+ enterprise AI deployments shipped. Engagement contracts start at one developer; most clients scale to 5–15.

Supported by Leading Tech & Growth Partners

Founded by Mitesh Patel — NVIDIA Certified AI Architect · Upwork Top Rated Plus (Individual Profile) →

— Market context · Talent gap · Why external

The AI Talent Gap Is Now the Single Largest Barrier to Production AI

Enterprises hire external AI developers because the in-house path now takes 6–9 months to fill a single senior AI seat, fully loaded compensation in the US is past $300,000, and most AI projects fail in the gap between research-grade code and production deployment. An external AI engineering team closes those three gaps without the recruiting overhead.

The global AI talent supply has fallen further behind demand. McKinsey puts the gap at roughly 50%: enterprises in North America and Western Europe can fill only one in two AI seats they need, even at premium compensation. LinkedIn data has the average time-to-hire for a senior AI engineer in the US sitting at 156 days. Compensation has moved with the scarcity: total comp for a senior AI engineer in a Tier-1 US metro now lands at $280,000–$350,000 fully loaded. A 5-engineer in-house team is therefore a $1.5M annual commitment before a single model ships.

Hence: 92% of enterprises have already integrated some form of AI into operations, and 97% report difficulty hiring qualified AI talent (Second Talent, 2026 industry survey). Traditional recruitment has not kept up. The pipeline is too narrow at the senior end, AI compensation has decoupled from the rest of engineering salaries, and most internal recruiters cannot evaluate model architecture choices, MLOps maturity, or who has actually shipped a model to production versus prototyped one.

External AI engineering teams resolve the bottleneck on three fronts. On speed: a pre-vetted bench shortlists candidates inside 48–72 hours and gets an engineer embedded in 14 days, against the 6-month internal recruit. On cost: a senior offshore-blended AI engineer bills at $55–$120 per hour all-in, against $135–$170 fully-loaded for the equivalent US in-house seat. On production craft: enterprises buy from firms that have already shipped 50–100+ AI deployments, which is exactly the variable academic AI hires lack.

The right engagement also de-risks the whole exercise. A pure-play AI specialist firm operating under ISO 27001 controls, signing an enforceable NDA, and transferring 100% of model weights and source code at delivery sits in lower-risk territory than a freelancer marketplace placement, and ships faster than waiting two financial quarters for a recruiter to fill the seat.

97%

Enterprises report difficulty hiring qualified AI talent

156 Days

Average US time-to-hire for senior AI engineer

$280–350K

Fully loaded annual cost of senior US in-house AI engineer

~50%

AI talent supply-demand gap in North America & Western Europe

$184B

Global AI market in 2025; projected to triple by 2030

— Roles · Specializations · Bench coverage

Eight AI Engineering Specializations — Hire One Role or a Full Cross-Functional Team

Brainy Neurals’ 20-engineer specialist bench covers eight AI roles. Most engagements blend three to seven of them into one dedicated team. A generative AI build will pair a GenAI engineer with an MLOps engineer and an AI architect; a computer vision deployment will pair CV engineers with edge-AI specialists and a data engineer. Every engineer is interviewed by Mitesh Patel, NVIDIA Certified AI Architect, before joining a client engagement.

Computer Vision Engineers

Detection · Segmentation · Tracking · OCR

What they build: Object detection, segmentation, tracking, OCR, defect detection, pose estimation, depth perception, 3D reconstruction, real-time multi-camera analytics.

TOOLS

YOLOv8, YOLOv11, Detectron2, MMDetection, OpenCV, Ultralytics, NVIDIA DeepStream, TensorRT, ONNX Runtime, MediaPipe, SAM2.

HARDWARE

NVIDIA Jetson Orin / Nano, Intel RealSense, Stereolabs ZED, Ouster Lidar, Qualcomm SNPE, Kneron.

Rate $55–$95/hr

Min 1 month · 160 hrs

Generative AI / LLM Developers

Fine-tuning · RAG · Conversational AI

What they build: Custom LLM fine-tuning, RAG architectures, conversational AI, multi-agent systems, prompt-engineering pipelines, model evaluation, content generation systems.

TOOLS

OpenAI GPT-4o, Claude Sonnet 4, Gemini 2.5, Llama 3.3, Mistral Large, Qwen, LangChain, LangGraph, LlamaIndex, Pinecone, Weaviate, Chroma, MCP servers.

Frameworks

Hugging Face Transformers, vLLM, TGI, Unsloth, LoRA / QLoRA, DPO / RLHF.

Rate $65–$120/hr

Min 1 month · 160 hrs

MLOps Engineers

Pipelines · Drift · Retraining · Inference at scale

What they build: Model deployment pipelines, drift detection, retraining loops, model registries, A/B testing, real-time inference at scale, observability for ML systems.

TOOLS

MLflow, Weights & Biases, ClearML, NVIDIA Triton Inference Server, KServe, Seldon Core, Kubeflow, Airflow, Prefect, Evidently AI, Fiddler.

cloud

AWS SageMaker, GCP Vertex AI, Azure ML, Kubernetes, Terraform, Helm, ArgoCD.

Rate $70–$110/hr

Min 1 month · 160 hrs

NLP Engineers

Document AI · IDP · NER · Semantic search

What they build: Document AI / IDP, named-entity recognition, intent classification, semantic search, content moderation, multi-language pipelines, voice-to-text, text-to-voice.

TOOLS

spaCy, Hugging Face Transformers, BERT / RoBERTa / DeBERTa, Sentence-Transformers, FAISS, Qdrant, Whisper, ElevenLabs, Deepgram.

domains

BFSI document workflows, healthcare clinical text, legal contract analysis, insurance claim processing.

Rate $60–$100/hr

Min 1 month · 160 hrs

Edge / Embedded AI Engineers

Jetson · Quantization · Real-time inference

What they build: Model deployment pipelines, drift detection, retraining loops, model registries, A/B testing, real-time inference at scale, observability for ML systems.

TOOLS

NVIDIA Jetson Orin / Nano / Xavier, Qualcomm SNPE SDK, Kneron, Rockwell, Intel RealSense, Stereolabs ZED, Ouster Lidar, TensorRT INT8 / FP16, OpenVINO, Hailo-8.

Rate $75–$110/hr

Min 1 month · 160 hrs

AI Solution Architects

System design · Tech selection · Roadmap

What they build: AI system architectures, technology stack selection, integration design, model selection & evaluation strategy, roadmaps, build-vs-buy decisions, cross-functional planning with engineering & business stakeholders.

Engagement

Typically engaged 4–20 hours per week alongside delivery teams.

Founder-led

Mitesh Patel personally architects every engagement. M.Tech Embedded Systems, NVIDIA Certified AI Architect, 9+ years exclusive AI focus.

Rate $110–$180/hr

Engaged part-time

Data Engineers (AI-specific)

Pipelines · Vector DBs · Embedding workflows

What they build: Training data pipelines, embedding stores, ETL for unstructured data, vector database design, feature stores, real-time streaming for inference, data versioning & lineage.

TOOLS

Apache Spark, Apache Kafka, dbt, Airflow, Snowflake, Databricks, Delta Lake, Iceberg, Tecton, Feast, Pinecone, Weaviate, Postgres pgvector.

Differentiation

Many “data engineers” cannot handle the unstructured-data and embedding-pipeline workflows AI systems need. Brainy Neurals’ data engineers come from AI projects, not ETL backgrounds.

Rate $60–$100/hr

Min 1 month · 160 hrs

AI Agent / Copilot Developers

Multi-agent · Tool-calling · MCP · Stateful

What they build: Multi-agent orchestration, tool-calling agents, MCP-fluent integrations, stateful agentic workflows, in-product copilots, autonomous task agents, evaluation harnesses for non-deterministic systems.

TOOLS

LangGraph, CrewAI, Microsoft AutoGen, Anthropic MCP, OpenAI Assistants API, Google ADK, Pydantic AI, DSPy.

Why distinct

Agentic systems need different design discipline than standard GenAI: graphs over chains, evaluation over assertions, recovery loops over try/catch. Few have shipped agents to production.

Rate $80–$130/hr

Min 1 month · 160 hrs

Every Brainy Neurals engineer carries 9+ years of exclusive AI focus on the company’s track record, ships production code under ISO 27001 controls, and signs the NDA before any project information is shared. The bench is intentionally small at 20 engineers. The 70+ enterprise AI projects delivered came from this team, not from a larger one.

— Dedicated · Staff aug · Project · Trial-to-hire

Four Engagement Models — Picked Based on Your Risk Profile and Timeline

Brainy Neurals offers four engagement models. Most enterprises open with a 2-week paid trial, then convert into either a dedicated team contract (full-time engineers, monthly retainer) or a staff augmentation contract (1–3 engineers embedded inside the client’s existing engineering team and managed by the client). Project-based fixed-cost work is reserved for clearly-scoped POC and MVP engagements where the deliverable is well-defined.

Model 1

recommended start

2-Week Paid Trial

Low-risk validation before any larger commitment. Standard at premium AI staffing platforms; Brainy Neurals offers it on every new engagement.

size

1–3 engineers

Duration

80–240 hrs

Cost

$4.4K–$19.2K

Convert

50% credit to month 1

Model 2

Dedicated Team

Multi-month or multi-quarter AI program. Stable, named team with continuity, low coordination overhead, clear team economics.

size

4–10 engineers

Pricing

$40K–$135K/mo

Min term

3 months

Swap

5 biz days · zero cost

Model 3

Staff Augmentation

You have an internal AI team and need 1–3 specialists to fill a specific skill gap (CV, MLOps, GenAI, edge) without expanding headcount.

size

1–3 engineers

Pricing

Hourly · per role

Min term

1 months

Swap

Client-owned · client-led

Model 4

Project · Fixed Price

Clearly-scoped POC, MVP, or single-deliverable engagement. Success criteria, dataset, and acceptance test defined upfront.

poc

$25K–$60K · 4–8 wks

mvp

$60K–$150K · 8–14 wks

Hardening

$40K–$120K

Risk

BN carries delivery risk

— Models · Frameworks · Infra · 50+ named technologies

The Tools Your Hired AI Developers Will Actually Ship With

Brainy Neurals’ AI engineers ship in production with the technologies named below. This is not a marketing aspiration list. Every category here has at least one ongoing client engagement behind it. Tools without an active production deployment are excluded.

Models / Frameworks

Foundation: GPT-4o, Claude Sonnet 4 / Opus 4, Gemini 2.5, Llama 3.3 (8B / 70B), Mistral Large, Qwen, DeepSeek V3. Vision: YOLOv8 / v11, Detectron2, MMDetection, SAM2, DINOv2, Grounding-DINO, OpenCV, MediaPipe. NLP: BERT, RoBERTa, DeBERTa, Sentence-Transformers, Hugging Face Transformers, spaCy. DL: PyTorch, TensorFlow, JAX, ONNX.

Languages

Python (primary), C++ (Edge / Embedded inference), Rust (high-throughput ML services), Go (MLOps tooling), CUDA / C++ (custom kernels), TypeScript (front-end + agent UIs), SQL (data engineering)

Edge & Embedded

NVIDIA Jetson Orin / Nano / Xavier, NVIDIA DRIVE, Qualcomm SNPE SDK, Kneron KL-series, Rockchip RK-series, Intel RealSense D-series, Stereolabs ZED 2 / ZED X, Ouster OS-1 Lidar, Velodyne VLP-16, Coral TPU, OpenVINO, Hailo-8

Cloud & Infrastructure

AWS (SageMaker, Bedrock, EC2 P-series, ECS, EKS, Lambda), Google Cloud (Vertex AI, GKE), Azure (ML Studio, AKS), NVIDIA NGC, Lambda Labs, RunPod, Paperspace, Modal

Data & Vector DB

Apache Spark, Apache Kafka, Apache Airflow, Prefect, dbt, Snowflake, Databricks, Delta Lake, Apache Iceberg. Vector DB: Pinecone, Weaviate, Qdrant, Milvus, Chroma, Postgres pgvector. Feature stores: Tecton, Feast

MLOps & Monitoring

MLflow, Weights & Biases, ClearML, NVIDIA Triton Inference Server, KServe, Seldon Core, BentoML, vLLM, TensorRT, TensorRT-LLM, Kubeflow, Evidently AI, Fiddler, Arize, WhyLabs, Prometheus + Grafana

Agent & GenAI Frameworks

LangChain, LangGraph, LlamaIndex, CrewAI, Microsoft AutoGen, Anthropic Model Context Protocol (MCP), OpenAI Assistants API, Pydantic AI, DSPy, Unsloth, LoRA / QLoRA, RLHF / DPO, vLLM, TGI

Security & Compliance

ISO 27001 (Brainy Neurals certified). HIPAA / GDPR / SOC 2-aware project setup. Identity: Okta, Auth0, AWS Cognito. Secrets: HashiCorp Vault, AWS Secrets Manager. Integration: REST, GraphQL, gRPC, webhooks, Salesforce / HubSpot / Dynamics / SAP / Workday connectors. PII redaction, prompt injection guardrails, model evaluation harnesses

If your in-house tech-radar lists a tool here, an engineer on the Brainy Neurals bench has shipped with it. If a tool you depend on isn’t listed, raise it on the discovery call. Mitesh Patel reviews bench coverage every quarter and we’d rather decline a tool we haven’t shipped with than claim generic coverage.

— Discovery · Match · Interview · Trial · Onboard

From Discovery Call to Embedded Engineers in 14 Days

Brainy Neurals’ five-step hiring process moves an enterprise from first contact to embedded engineers in 14 calendar days. The same five steps appear in the JSON-LD HowTo schema published with this page. Steps 1–3 run sequentially and are time-bounded. Step 4 (the trial) and Step 5 (onboarding administration) overlap so the engineer is fully productive when the trial period starts.

Discovery

Day 0

30-minute architecture call with Mitesh Patel and a senior architect. Use case scoped, success criteria captured, role mix confirmed.

Match

Day 1–2

Bench review against confirmed roles. Shortlist of 2–3 candidates per role assembled. CVs and named project references shared in 48–72 hrs.

Interview

Day 3–5

Client interviews shortlisted engineers (45–60 min technical interview each). Optional take-home task. Final selection by client.

Trial Begins

Day 7–10

Selected engineers begin paid 2-week trial against a real project. First demo at end of week 1. Daily standups attended.

Embed

Day 11–14

End-of-trial review. If converted, dedicated team or staff aug contract activates immediately into Day 15+. No re-onboarding.

Architect a Team in 30 Minutes. Ship in 14 Days. Scale in 30.

Mitesh Patel and a senior architect will join your call. We’ll size the team, name the engineers we’d embed, and give you a written engagement plan within 24 hours. No commitment required.

48–72 hrs

Shortlisted engineer profiles delivered

14 days

From first call to embedded engineer

5 biz days

Engineer swap window — at zero cost

100%

IP transfer at delivery — every line, every weight

— Domain-experienced engineers · Cross-linked industry depth

Six Industry Verticals With Shipped Production AI

Brainy Neurals’ engineers are not generic Python developers re-labeled as AI talent. Every engineer has deployed AI in at least two of the verticals below. When an engagement begins, the matched team includes engineers with direct production experience in the client’s vertical, not adjacent. A 20-engineer specialist firm with 70+ shipped projects beats a 1,000-engineer generalist firm at vertical-specific AI delivery, because in the generalist firm the share of engineers who’ve actually shipped to that vertical is statistically thin.

Manufacturing & Industrial

Quality inspection, defect detection on production lines, predictive maintenance from sensor data, OCR for industrial labels, worker-safety vision, yield optimization. Engineers ship on Jetson Orin / DeepStream / TensorRT-INT8 stacks for sub-100ms factory-floor inference.

See industry page →

BFSI · Banking, Insurance, Financial Services

Document understanding for loan and insurance workflows, KYC automation, intelligent claims triage, fraud detection, transaction monitoring, customer-service GenAI assistants. Engineers know PII redaction, on-prem inference, audit-ready model evaluation.

See industry page →

Healthcare & Life Sciences

Medical imaging classification and segmentation, radiology workflow assistants, clinical-text NLP, pharma quality assurance, EHR-aware copilots, HIPAA-aligned project setups. Engineers work daily with DICOM, FHIR, de-identification pipelines.

See industry page →

Logistics & Supply Chain

Warehouse automation vision, dimensioning and load-validation, route optimization, demand forecasting, last-mile inspection, OCR for shipping labels, fleet vision. Edge deployments on rugged Jetson hardware in distribution centres.

See industry page →

Construction & Infrastructure

Drone-based progress tracking, BIM-integrated vision, on-site safety monitoring, plan-approval automation, equipment utilization analytics, infrastructure inspection. Engineers run multi-modal sensor fusion (RGB + Lidar + GPS) for outdoor and field deployments.

See industry page →

Sports & Media

Player tracking and biomechanics, broadcast-grade video analytics, automated highlights, performance benchmarking, fan-engagement copilots. Engineers have shipped real-time multi-camera systems with sub-frame synchronization.

See industry page →

If your industry is not listed: aerospace, defense, energy, agriculture, and retail are available case-by-case. The discovery call will confirm whether the bench has shipped to your vertical, or whether you’d be better served by a domain specialist firm. We will tell you honestly if it’s the latter.

— Transparent bands · Published rates · No hidden fees

What Hiring an AI Developer Actually Costs in 2026

Hiring a senior AI developer through Brainy Neurals costs $65–$120 per hour all-in. Hiring the equivalent in-house engineer in a US Tier-1 metro costs $135–$170 per hour fully loaded once compensation, recruitment, taxes, benefits, equipment, and recruitment-cycle opportunity cost are accounted for. A 5-engineer dedicated team with Brainy Neurals runs $50K–$70K per month all-in; the equivalent in-house team runs $115K–$145K per month fully loaded. Across a 12-month program that’s roughly $780K–$900K saved at the same quality level, with a faster ramp.

Hourly rate bands by role

Bands are full-stack: Brainy Neurals’ bill rate to the client. They cover engineer compensation, project management, ISO 27001 compliance overhead, the secure development environment, NDA enforcement, and the engineer-swap guarantee. There are no hidden fees and no upcharges for tooling.

Role	Junior	Mid	Senior	Notes
Computer Vision Engineer	$55–$65	$65–$80	$80–$95	Edge / Jetson specialization +10%
Generative AI / LLM Developer	$65–$75	$75–$95	$95–$120	Agentic experience commands top of band
MLOps Engineer	—	$70–$90	$90–$110	Kubernetes + Triton at scale required for senior
NLP Engineer	$60–$70	$70–$85	$85–$100	Document AI / regulated workflows
Edge / Embedded AI Engineer	—	$75–$95	$95–$110	Rare specialization industry-wide
AI Solution Architect	—	—	$110–$180	Architects only · 4–20 hrs/week
Data Engineer (AI-specific)	$60–$70	$70–$85	$85–$100	Embedding pipelines · vector DB
AI Agent / Copilot Developer	—	$80–$100	$100–$130	Highest demand · scarcest bench in 2026

Policy on juniors: A junior engineer is never placed without a senior engineer or architect on the same engagement. A junior engineer leading a production AI deployment is one of the most common failure modes in cheaper-marketplace engagements, and Brainy Neurals avoids it on principle.

Total cost of ownership — 12-month, 5 senior engineers

Cost Driver	In-House US Hire	Big-4 Consulting	Freelance Marketplace	Brainy Neurals
Senior AI engineer / hour	$135–$170 fully loaded	$220–$400 rate-card	$80–$200 (varies wildly)	$65–$120 published bands
Time to first engineer producing	4–7 months	4–8 weeks	1–3 weeks (variable)	14 days (paid trial start)
Recruitment fees	20–25% of base	Built into rate	Platform fee 10–25%	$0
Vetting & quality control	Internal hiring panel	Firm reputation	Self-managed	Founder-interviewed · swap policy
IP transfer at delivery	Employee retains tacit knowledge	Per contract — varies	Per platform — risky	100% transferred
Compliance posture	Per company maturity	Audit-ready	None — client carries risk	ISO 27001 · HIPAA / GDPR / SOC 2-aware
Engineer swap if underperforming	Termination + re-recruit (3–6 mo)	Per partnership tier	Self-managed	5 biz days · zero cost
12-month cost — 5 senior	$1.4M–$1.8M	$2.4M–$4.2M	$0.85M–$2.0M	$650K–$960K all-in

$780K

Typical 12-month savings vs. in-house US AI team. Modeled on a 5-engineer dedicated team. Comparison includes recruitment, taxes, benefits, equipment, and ramp-time opportunity cost.

156 days

saved vs. average US senior AI hire cycle

70+

shipped enterprise AI projects in the deployed portfolio

specialist engineers · founder-interviewed bench

9 yrs

exclusive AI focus, founded 2018

Stop waiting six months for one in-house seat. Embed an entire team in 14 days.

30-minute architecture call with Mitesh Patel and a senior architect. We’ll show you the engineers we’d assign, with named project references.

— Shipped · Production · Named technologies · Verifiable outcomes

Three Production Deployments Built by Hired Brainy Neurals Engineers

Three engagements selected from the 70+ shipped portfolio. Each was delivered by a dedicated team or staff augmentation engagement. All three are in production today. Client identities are anonymized to industry tier; full deployment write-ups, named references, and architecture diagrams are available under NDA on the discovery call.

8% false-pos

99.2%

accuracy 200 fps

Manufacturing

Real-Time Defect Detection — NYSE-Listed Industrial Manufacturer

Team: 4 engineers (2 CV, 1 MLOps, 1 Edge AI) · 16-week dedicated team engagement

PROBLEM

Aluminum extrusion line was running 8% defect false-positive rate, costing roughly $4M annually in scrapped product and operator review time. Existing rule-based machine vision could not generalize across alloy variations.

Approach

YOLOv8 fine-tuned on 47K labeled defect samples across 6 alloy types. TensorRT INT8 quantization for sub-5ms inference on Jetson Orin AGX. NVIDIA DeepStream pipeline with 4-camera synchronization. MLflow + Triton for retraining and rollout.

Stack: YOLOv8 · TensorRT INT8 · NVIDIA Jetson Orin AGX · NVIDIA DeepStream · NVIDIA Triton · MLflow · PyTorch · OpenCV · Kubernetes (control plane)

Outcome: 99.2% defect detection at 200 fps. False-positive rate fell from 8% to 1.4%. $2.1M annual labour reallocation. Trial-to-production: 11 weeks.

61%

analyst hrs auto

BFSI

Loan Underwriting Copilot — US Mid-Market Commercial Lender ($11B AUM)

Team: 5 engineers (2 GenAI, 1 NLP, 1 MLOps, 1 Architect) · 14-week dedicated engagement

PROBLEM

22 commercial loan analysts spent ~70% of their time extracting and cross-referencing financial covenants from loan packets. Backlog of 4–6 weeks. Mid-market deal flow constrained by analyst capacity, not by demand.

Approach

Hybrid Claude Sonnet 4 + on-prem Llama 3.3 70B (PII-sensitive workloads). LangGraph agentic workflow. Pinecone for clause retrieval across the lender’s 12-year underwriting history. Custom 1,200-case eval harness. MCP loan-origination integration.

Stack: Claude Sonnet 4 · Llama 3.3 70B (on-prem vLLM) · LangGraph · Pinecone · MCP servers · Custom eval harness · Prometheus / Grafana monitoring · SOC 2-aligned audit log

Outcome: 61% of weekly review hours automated at 95.3% precision against the eval target. Audit log fully integrated with the firm’s compliance system. Trial-to-production: 9 weeks. The 22 analysts re-skilled into higher-value risk-assessment roles.

73%

PPE violations ↓

Construction · EU

Multi-Site Safety Vision — EU Tier-1 Construction Group, 6 Countries, $2.8B Revenue

Team: 6 engineers (3 CV, 1 Edge AI, 1 Data, 1 MLOps) · 22-week dedicated engagement

PROBLEM

21 active construction sites across 6 EU countries with safety-incident insurance claims rising 19% YoY. Manual PPE compliance audits caught roughly 30% of violations. Insurer flagging the program for premium review.

Approach

YOLOv11 fine-tuned for hard-hat / vest / boot detection plus exclusion-zone monitoring. TensorRT INT8 on Jetson Orin Nano per site. Ouster OS-1 Lidar sensor fusion for 3D zone-violation detection. GDPR-compliant on-prem inference (no worker imagery leaves site).

Stack: YOLOv11 · TensorRT INT8 · NVIDIA Jetson Orin Nano · Ouster OS-1 Lidar · ZED stereo camera · ROS 2 · MQTT (site-to-cloud) · Grafana operational dashboards · GDPR-compliant DPA

Outcome: PPE violations reduced 73% across 21 sites in 4 months. Zone violations reduced 41%. Insurance carrier removed premium review flag. ~$890K annual insurance savings + immeasurable goodwill from zero serious-injury quarter.

Capability footprint

Hire AI Developers Who Have Already Built What You’re Trying to Ship

70+ enterprise AI projects shipped. NVIDIA Inception Partner. AWS Activate. Microsoft for Startups. ISO 27001 Certified. Founder is an NVIDIA Certified AI Architect. The bench is intentionally small — 20 specialists.

— BN vs. In-house · Staffing · Marketplace

Brainy Neurals vs. The Three Other Ways You Could Hire AI Developers

Honest comparison across 13 decision factors. Where another option is the better fit, this page will say so directly. Brainy Neurals will not pitch itself as the right firm for a 50+ engineer engagement, a single sub-4-week freelancer, or a permanent in-house seat. The disclosure block below the table makes the boundaries explicit.

Decision Factor	In-house Recruiting	IT Staffing Firm	Freelance Marketplace	Brainy Neurals
AI specialization	Per hire	Generalist · AI is one practice	Self-vetted	Pure-play AI · 9 yrs exclusive focus
Time to first productive engineer	4–7 months	4–8 weeks	1–3 weeks (variable)	14 days
Engineer interviewed by founder	—	—	—	Yes — Mitesh Patel personally
Production AI deployments shipped	Per engineer	Variable	Per engineer	70+ at firm level
Hourly rate (senior)	$135–170 fully loaded	$130–250 rate-card	$80–200 (wide variance)	$85–130
Engineer swap if underperforming	3–6 mo termination cycle	Per partnership tier	Self-managed	5 biz days · zero cost
IP transfer at delivery	Tacit retention	Per contract	Per platform terms	100% — every line, every weight
ISO 27001 / GDPR / HIPAA / SOC 2	Per company	Per firm	Self-managed	ISO 27001 certified · others aligned

Where Brainy Neurals is not the right choice

If you need a single engineer for under 4 weeks: a freelance marketplace will be faster to transact and have broader supply. Brainy Neurals’ 14-day engagement-start cycle is calibrated for multi-quarter dedicated teams, not one-off tasks.
If you need 50+ engineers across multiple non-AI engineering practices: a large IT staffing firm or systems integrator is structurally better. A 20-engineer specialist firm is the wrong shape for that scope.
If you need a permanent in-house hire with deep institutional integration over 5+ years: hire in-house. Brainy Neurals’ value is in the engagement timeframe of 3–24 months, not in replacing permanent senior AI leaders.

— Six structural reasons enterprise buyers pick BN

Why Pure-Play AI Specialist Firms Win Enterprise AI Engagements

Six reasons enterprise procurement teams pick Brainy Neurals over a marketplace, a generalist staffing firm, or an in-house build. Each is verifiable on the discovery call.

Pure-Play AI Specialist

20 engineers — every one of them shipping AI in production. Not a Java shop with an “AI practice” tacked on. 9 years exclusive AI focus since 2018, beginning with NVIDIA DeepStream + YOLOv2 deployments before the GenAI cycle ever existed.

Founder-Architected

Mitesh Patel — NVIDIA Certified AI Architect, M.Tech Embedded Systems, Upwork Top Rated Plus (top 3% globally) — joins the discovery call on every new engagement and personally architects the team composition. Most enterprise software-services firms route prospects through a sales engineer; Brainy Neurals routes them through the technical founder.

70+ Production AI Projects

Firm-level proof, not just engineer-level résumés. The portfolio spans manufacturing, BFSI, healthcare, logistics, construction, and sports, with named technologies on every deployment (YOLOv8, Triton, TensorRT, LangGraph, Pinecone, Jetson, Claude, Llama). It’s also why engineer matches happen quickly: the firm has shipped your use case before, on the technology you intend to use.

ISO 27001 + Regulated-Workload Ready

ISO 27001 certified at the firm level. Project workflows for healthcare clients are HIPAA-aligned. EU engagements run GDPR-aware data handling. BFSI workloads use SOC 2-aware controls. NVIDIA Inception Partner, AWS Activate Startup Ecosystem, Microsoft for Startups. The compliance posture is what makes Brainy Neurals a viable partner for regulated enterprise procurement, not just for ad-hoc projects.

100% IP Transfer · No Lock-In

The client owns 100% of intellectual property at delivery: source code, model weights, training scripts, evaluation harnesses, infrastructure-as-code, prompts, configurations, and documentation. Brainy Neurals retains no rights, licenses nothing back, and does not require ongoing dependency. Operate the system on Day 1 of handover or re-engage Brainy Neurals on a new SOW — by choice, not by lock-in.

5-Day Engineer-Swap Guarantee

If a placed engineer underperforms, Brainy Neurals replaces them within 5 business days at zero cost to the client. The industry rarely offers this. Most firms either renegotiate the engagement or charge for the swap; freelance marketplaces leave the client to manage it. The guarantee de-risks the engagement to roughly the level of an internal hire, without the recruitment-cycle cost.

“We’ve kept the bench at 20 specialists on purpose. Every engineer goes through me before joining a client engagement. The reason this firm has shipped 70+ enterprise AI projects in 9 years is the same reason we’ll never have 1,000 engineers — depth of craft doesn’t scale linearly with headcount, and most of the failure modes I see in enterprise AI come from teams that grew engineers faster than they grew production discipline.”

— Mitesh Patel

Founder · Brainy Neurals

● NVIDIA Certified AI Architect

— FAQ

Frequently Asked Questions

How much does it cost to hire an AI developer through Brainy Neurals?

Hiring an AI developer through Brainy Neurals costs between $55 and $130 per hour, depending on role and seniority. Junior developers (under 3 years of production experience) bill at $55–$75 per hour. Mid-level developers (3–6 years) bill at $65–$95. Senior engineers (6+ years) bill at $85–$130. AI Solution Architects bill at $110–$180 per hour and are usually engaged 4–20 hours per week alongside delivery teams. Edge AI, agentic systems, and senior MLOps command top-of-band rates because the bench for those skills is shallow across the industry. Rates are all-inclusive: no separate fees for tooling, project management, security overhead, or engineer swaps. Infrastructure (GPU compute, third-party model API costs, specialized hardware) is procured by the client at-cost and does not sit inside the bill rate.

How long does it take to hire AI developers and embed them in our team?

Brainy Neurals’ standard hiring timeline is 14 calendar days from the first discovery call to embedded engineers actively working. The five-step process: Day 0, a 30-minute architecture call with Mitesh Patel and a senior architect. Day 1–2, bench review and a shortlist of 2–3 candidates per role with CVs and named project references. Day 3–5, client-led interviews and final selection. Day 6, MSA, SOW, and NDA executed and tool access provisioned. Day 7–14, the 2-week paid trial with the selected engineers, ending in conversion to a dedicated team or staff augmentation contract on Day 15. Compared to the typical 4–7 month US in-house senior AI hire cycle, that’s roughly 12× faster to a productive engineer.

What roles can we hire and what is the minimum engagement size?

Eight specialist AI roles are available: Computer Vision Engineers, Generative AI / LLM Developers, MLOps Engineers, NLP Engineers, Edge / Embedded AI Engineers, AI Solution Architects, Data Engineers (AI-specific), and AI Agent / Copilot Developers. The minimum engagement is 1 month (160 hours) for staff augmentation, 3 months for a dedicated team contract, or per-SOW for fixed-price projects. Most enterprises starting with Brainy Neurals open with a 2-week paid trial of 1–3 engineers (80–240 hours total) and convert to a dedicated team or staff augmentation contract at trial end. The trial-conversion incentives are spelled out in the Engagement Models section above.

How does Brainy Neurals compare to platforms like Toptal, Turing, or Upwork?

Brainy Neurals is a pure-play AI specialist firm with a 20-engineer bench, a founder who personally architects every engagement, and ISO 27001 certification. Toptal, Turing, and Upwork are talent marketplaces, with wider pools of independent freelancers and platform-managed vetting. Which one is right depends on the engagement profile. For a single freelancer for under 4 weeks, the marketplaces are faster to transact and have broader supply. For an enterprise AI program running multi-quarter with 3–15 engineers under compliance constraints, marketplaces leave the client to manage vetting, integration, swap risk, and IP transfer. The comparison table in this page walks through 13 decision factors. Short version: marketplaces win on transactional speed and supply breadth; specialist firms win on production craft, governance, and engineer-quality consistency.

How is intellectual property handled? Do we own the code and model weights?

The client owns 100% of intellectual property at delivery: all source code, every model weight file, all training scripts, evaluation harnesses, infrastructure-as-code, prompts, configurations, and documentation. Brainy Neurals retains no rights, licenses nothing back, and does not require ongoing dependency on Brainy Neurals to operate the system. The IP transfer is contractual and is enforced by the MSA signed at engagement start. This is the opposite of platform-based AI vendors who retain model weights and license access. Clients who want to operate the AI system themselves after delivery can do so on Day 1 of handover. Clients who want continued support or extension work re-engage Brainy Neurals on a new SOW, by choice rather than by lock-in.

What about compliance — ISO 27001, HIPAA, GDPR, SOC 2?

Brainy Neurals is ISO 27001 certified at the firm level. Project setups for healthcare clients are HIPAA-aligned, including PHI handling, BAA execution where required, and audit-trail enforcement. EU project setups are GDPR-aware, including data residency, right-to-erasure compliance, and DPA execution. BFSI project setups are SOC 2-aware, including separation-of-duties controls, secure-development-lifecycle enforcement, and PII redaction in model inputs and outputs. The firm is not FedRAMP-authorized. Clients with US federal-government workload requirements should engage a FedRAMP-authorized partner. Procurement teams asking for that scope-of-coverage clarity on the discovery call are exactly who this page is for.

What happens if a placed engineer underperforms?

Brainy Neurals replaces an underperforming engineer within 5 business days at zero cost to the client. This is the engineer-swap guarantee written into every MSA. The replacement comes from the same bench, is interviewed by the client if requested, and ramps onto the engagement using the handover documentation Brainy Neurals maintains internally. The 5-day window covers replacement identification, client interview if any, NDA execution, and tool access: the same path as initial onboarding, accelerated by the existing engagement context. Across 70+ shipped engagements, swaps have been rare (under 5% of placements), but the policy exists because de-risking the engagement at this level is what enterprise procurement asks for first when evaluating external AI teams.

What time zones do Brainy Neurals engineers operate in?

Engineers maintain about 70% overlap with US Eastern Time and roughly 100% overlap with European Central Time across a typical workday. Standup, code review, demo, and pairing windows are scheduled inside the overlap. Synchronous work hours are typically 12:00–18:00 GMT for US ET clients and 09:00–17:00 GMT for EU clients. Asynchronous work continues outside the overlap window using Slack, Linear / Jira, GitHub PR review, and shared design docs. On engagements that need deeper US ET overlap, engineers shift schedules and there is no surcharge for the shift. ANZ and Pacific time-zone overlaps are handled case-by-case on the discovery call.

Can we start with a small engagement and scale up?

Yes. Most Brainy Neurals client relationships start small. The recommended path is a 2-week paid trial with 1–3 engineers focused on a real piece of work rather than a synthetic test. The trial converts to a dedicated team or staff augmentation contract on Day 15 if the fit holds. Scale-up happens through SOW amendments: additional engineers join the existing engagement with no fresh onboarding overhead. Across the 70+ shipped engagements the typical opening is 1–2 engineers, and the typical 60-day position is 4–6. The largest active engagement currently runs 14 engineers across two parallel use cases for a single client.

Why does Brainy Neurals only have 20 engineers when competitors have 1,000+?

Brainy Neurals’ bench is intentionally small. Every engineer is interviewed and approved by Mitesh Patel — NVIDIA Certified AI Architect and Founder — before joining a client engagement. Across 9 years of exclusive AI focus the firm has shipped 70+ enterprise AI projects with this team. A 20-engineer pure-play AI specialist with deep production experience in computer vision, generative AI, MLOps, edge AI, and AI agents will outperform a 1,000-engineer generalist firm whose AI practice is one of many. That holds for the engagements this page targets: multi-quarter, 3–15 engineers, regulated workloads, where production craft matters. For engagements that need 50+ engineers across multiple non-AI engineering practices, Brainy Neurals isn’t the right firm and we’ll say so on the discovery call.

— Adjacent capabilities · Dee service pages

If You're Evaluating an AI Engineering Team, These Are the Adjacent Service Pages

Click into any one for deep technology detail, named tools, methodology, and case studies for that specific service.

Service · 01

AI POC & MVP Development

Fixed-cost AI proof-of-concept and MVP engagements with clear acceptance criteria, dataset definition, and success thresholds. 4–14 week deliveries with go/no-go feasibility reports built in.

View service page →

Service · 02

AI Consulting & Strategy

Founder-led architecture reviews, build-vs-buy analyses, AI roadmaps, and technology selection for enterprise leaders evaluating AI investments without committing to a delivery team yet.

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Service · 03

Computer Vision Development

Deep specialization page for object detection, automated visual inspection, 3D reconstruction, depth sensing, and edge AI deployment on NVIDIA Jetson, Intel RealSense, Stereolabs, and Lidar stacks.

View service page →

Service · 04

Generative AI Development

Custom LLM fine-tuning, RAG architectures, AI agent and copilot systems, conversational AI. Production-grade GenAI engineering with named-model evaluation and on-prem deployment options.

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- Let’s Build AI for Your Everyday Challenges

Among the Top 3% of Global AI Professionals.

50+

AI SYSTEMS IN PRODUCTION

9+

YEARS IN PRODUCTION AI

Led by an NVIDIA Certified AI Architect. Backed by AWS, Microsoft & NVIDIA ecosystems. ISO 27001 certified for enterprise-grade security. Every call is a free technical assessment — not a sales pitch.

Or email: hello@brainyneurals.com