Enterprise AI services, architected and shipped.

Eleven services across Computer Vision, Edge AI, Generative AI, RAG, Agents, Document AI, and MLOps. One specialist firm with seventy-plus production deployments behind it.

Most AI consulting firms give you a strategy deck. We give you a strategy deck AND the engineering team that builds it. Our AI consulting services span readiness assessments, AI strategy consulting, use case identification and prioritization, technology selection, ROI modeling, and AI implementation consulting — delivered by an NVIDIA Certified AI Architect with 70+ production AI projects across computer vision, generative AI, RAG, edge AI, document AI, and video analytics. When you hire AI consultants at Brainy Neurals, the person advising your strategy is the same person who will architect your solution. Zero handoff gap between strategy and execution.

+70

Production AI Projects

8 Years

Exclusively in Applied AI

20

Specialist AI Engineers

NVIDIA

Certified AI Architect

Supported by Leading Tech & Growth Partners

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

— THE SERVICE MAP

Ten services in Four clusters.

BrainyNeurals delivers eleven enterprise AI services across four capability clusters: Vision Intelligence, Language and Generative AI, Strategic AI, and Edge and Hardware AI. Each cluster shares a deployment pattern (same data discipline, same MLOps backbone, same production hardening) so engagements that span multiple services run as one project, not three.

Cluster 01 · Vision Intelligence

Detection, classification, tracking, and counting from camera feeds. Operating in factories, warehouses, retail floors, perimeter security, traffic environments, and construction sites. Eight years of computer vision deployments across edge and cloud. This is our deepest practice and the source of most of our case studies.

Computer Vision Development

Object detection, instance segmentation, pose estimation, OCR, anomaly detection, and visual quality inspection. Built on YOLO, Detectron2, MMDetection, and custom transformer architectures. Deployed across more than 30 production CV systems.

Vision Intelligence cluster →

Video Analytics & Intelligent Surveillance

Real-time video pipelines for safety monitoring, intrusion detection, crowd analytics, and operational intelligence. Built on NVIDIA DeepStream and Triton with multi-camera fusion, cross-camera tracking, and forensic search.

Video analytics practice →

Cluster 02 · Language and Generative AI

Document understanding, generative content systems, retrieval pipelines, and autonomous agents. Where structured documents, free text, and decision-making automation meet. Typically the highest-ROI cluster for Banking, Insurance, Legal, and Healthcare administration.

Document AI & Intelligent Document Processing

Field-level extraction from invoices, claims, contracts, KYC documents, and regulatory filings. 97%+ accuracy with full audit trail and human-in-the-loop fallback. Architecturally compatible with ABBYY, Kofax, and Rossum incumbents.

Document AI practice →

Generative AI Development

LLM-powered systems for content generation, summarization, translation, and conversational interfaces. Model-agnostic, built on Claude, GPT, Gemini, Llama, and Mistral with provider abstraction so the underlying model can change without re-architecting.

Generative AI work →

RAG Development Services

Retrieval-augmented generation systems with enterprise-grade vector indexing, hybrid retrieval, and grounded answer generation. Built on Pinecone, Weaviate, Qdrant, and pgvector with custom retrieval logic per knowledge corpus.

RAG practice →

AI Agent & Copilot Development

Autonomous and semi-autonomous agents for workflow automation, internal copilots, and multi-step reasoning systems. Built on LangGraph, CrewAI, and custom agent orchestration with explicit state, retries, and human approval gates.

Agent & Copilot practice →

Cluster 03 · Strategic AI

The advisory and validation services that frame every production engagement. Used most often by enterprises that have validated AI value but need a structured path from idea to production, and by enterprises that have not yet validated value and need an honest assessment.

AI Consulting & Strategy

Readiness assessment, use-case prioritization, technology selection, ROI modeling, and phased implementation roadmaps. Delivered by engineers, not slide-deck consultants. The most common entry point for enterprises new to AI.

Strategic AI cluster →

AI Proof of Concept & Pilot Development

Four-to-six week proofs of concept on real client data. Architected for production from day one, so the POC code path becomes the production code path. Go/no-go recommendation at end of POC, including honest “do not proceed” recommendations when warranted.

Validate the use case →

Cluster 04 · Edge and Hardware AI

Where AI runs on devices, not on cloud GPUs. The cluster that requires the deepest hardware-software co-design experience, and the one where generalist firms struggle most. Eight years of NVIDIA Jetson, Qualcomm SNPE, Intel RealSense, ZED stereo, and custom embedded deployments.

Edge AI & Embedded AI Development

On-device inference for low-latency, bandwidth-constrained, or air-gapped environments. NVIDIA Jetson Orin/AGX, Qualcomm SNPE SDK, Rockchip, Kneron, and Triton-on-edge deployments. TensorRT optimization for sub-50ms inference budgets.

Edge & Hardware AI practice →

Robotics & Hardware Automation

Perception stacks for robotics, AGVs, AMRs, and automation cells. Stereo vision, lidar fusion, GPS-RTK integration, and ROS/ROS2-based control loops. Deployed in warehouses, manufacturing, and outdoor inspection environments.

Robotics practice →

If your problem maps to more than one cluster, that is expected. Most production engagements span at least two. Pick the cluster closest to your primary problem and start there; we will surface the cross-cluster dependencies during discovery.

— THE SPECIALIST THESIS

Enterprise AI is What We Do.

BrainyNeurals is an AI-only specialist firm. We do not run a web development practice on the side, we do not maintain a mobile app practice, and we do not staff a generic IT services bench. Every one of our 20 engineers works exclusively on production AI: Computer Vision, Edge AI, Generative AI, RAG, Agents, Document AI, and MLOps. The depth comes from the focus.

In 2018 we made a call: stop being a generalist software firm with an AI page, become an AI specialist with no other practice. Eight years later, that call is the thing we sell against larger competitors. A generalist agency with 1,600 engineers might have 80 doing AI in any given week. We have 20, and they have done nothing else for years. That difference shows up in week one of every engagement, not in our marketing materials. The framework for how to evaluate AI development partner choices comes down to depth versus breadth, and the four arguments below explain why depth wins for production AI work.

01

Depth comes from focus

Edge AI requires intimate familiarity with NVIDIA Jetson, Qualcomm SNPE, Triton inference server, TensorRT, and stereo vision pipelines. A generalist team picks one of those up per project. We have shipped on all of them. Eight years of working on the same problem class compounds. One-off project teams cannot replicate that.

02

Founder-led, not founder-marketed

Mitesh Patel (NVIDIA Certified AI Architect, M.Tech Embedded Systems, eight years applied AI) leads the architecture review on every engagement. Personally. Not as a brand asset, not as an advisory role. The architect who designs your system is the same architect who shows up in the production go-live call.

03

Production-grade by default

Most AI engagements at generalist firms ship a proof of concept that quietly never reaches production. Our POCs are architected for production from day one: same data pipeline, same inference stack, same monitoring layer. Going from our POC to our production deployment is a deployment exercise, not a re-architecture.

04

Same team end-to-end

Most AI engagements involve at least three handoffs: sales to architecture, architecture to build, build to deployment. Every handoff loses context, and lost context is where projects die. The architect who scopes is the architect who designs. The lead who builds the POC is the lead who hardens production.

— METHODOLOGY

How we go from Problem to Production.

Our AI development methodology runs in four phases: Discovery and Feasibility, POC Development, Production Deployment, and Continuous Optimization. Typical end-to-end AI development timeline is 16 weeks from kickoff to production go-live. Each phase has fixed timing, named deliverables, and an explicit go/no-go gate at its close. Engagements are inspectable at every stage, not opaque until the final demo.

Discovery & Feasibility

Week 1–2

POC Development

Week 3–6

Production Deployment

Week 7–9

Continuous Optimization

Week 9–11

PHASE 1 Discovery and Feasibility · Weeks 1-2

What we do

Two weeks understanding your data, your infrastructure, your existing systems, and the actual decision the AI will inform or automate. Most “discovery” engagements at generalist firms are sales calls in disguise. Ours are technical audits. The output is a feasibility report that an enterprise architect can defend to a CFO.

What you receive

Data audit covering schema, volume, quality, label availability, and gaps
Use-case validation: does AI actually solve this, or does a deterministic rule engine?
Technology selection covering model architecture, deployment target, and infrastructure prerequisites
ROI projection with quantified efficiency or revenue impact, and a sensitivity analysis
Risk register covering compliance, data residency, model fairness, and deployment dependencies

Go / No-go gate

At end of week 2 we deliver an explicit recommendation: proceed to POC, defer pending data preparation, or do not proceed. We have given “do not proceed” recommendations to clients ready to spend $300K. We do this because the alternative (building a POC on data that cannot support production) wastes everyone’s time.

PHASE 2 POC Development · Weeks 3-6

What we do

Four weeks to ship a working AI system on your real data. Not a Jupyter notebook. Not a slide deck. A functional system that takes your inputs and produces your outputs. The POC code path is the production code path; we do not write throwaway code. That is what makes the 16-week end-to-end timeline possible.

What you receive

Working POC deployed in your environment or our staging environment
Accuracy benchmarks on your data, with confusion matrix and edge-case analysis
Latency benchmarks on the target deployment hardware
Integration scaffolding: API contract, data ingestion pipeline, basic monitoring
Architecture documentation: what we built, why, and how it scales

Go / No-go gate

End of week 6, we present the POC against the success criteria defined in Phase 01. If the system meets those criteria, we proceed to Production. If it does not, we explain what would be required to meet them (additional data, architectural change, hardware upgrade) and you decide whether to invest. No sales pressure to proceed when results say otherwise.

PHASE 3 Production Deployment · Weeks 7-12

What we do

Six weeks to harden the POC into a production system. This is where most “AI POC graveyard” projects die: at the production handoff, when the team that built the POC is gone and a different team has to figure out what they built. We do not have this handoff. Same engineers, same architect, week 1 to week 12.

What you receive

Production deployment to your target environment (cloud, on-prem, edge, or hybrid)
Hardened inference pipeline with error handling, retry logic, and fallback paths
Monitoring layer: accuracy drift detection, latency monitoring, alert routing
Integration to upstream and downstream systems (CRM, ERP, SCADA, BMS, VMS, data warehouse)
Security and compliance hardening: encryption, audit logging, access controls
Runbook and operational documentation for your internal team

Go / No-go gate

End of week 12, production go-live. Acceptance criteria are met before sign-off; we do not declare a system production-ready until your team has signed acceptance against the criteria from Phase 01.

PHASE 4 Continuous Optimization · Ongoing

What we do

Production AI systems drift. Models trained on 2024 data will degrade on 2026 data. Cameras get repositioned, business processes change, edge cases emerge. Phase 04 is the support model that keeps the system performing. Not a maintenance contract. An active retraining and expansion engagement.

What you receive

Quarterly model retraining with refreshed data
Drift monitoring with proactive alerting before accuracy drops
Expansion to new use cases: adding new detection classes, document types, languages, or sites
Quarterly executive review covering performance against KPIs, expansion opportunities, and next-quarter priorities

— ENGAGEMENT MODELS

Four engagement models. Pick the one that matches your stage.

We offer four AI engagement models scaled to enterprise stage and risk appetite: AI Readiness Assessment, POC Sprint, End-to-End Project, and Dedicated AI Team. Each model has fixed scope, fixed timing, and an explicit success definition. You know exactly what you are buying and exactly when you have it.

Engagement Model	When to Choose	What You Receive	Typical Timeline	Our Commitment
AI Readiness Assessment	You are considering AI but do not know where to start, whether your data is ready, or which use case has the highest ROI.	AI readiness scorecard across 5 dimensions (data, infrastructure, organization, use case, compliance). Prioritized use-case portfolio. Go/no-go recommendation per use case.	2-4 weeks	We do not auto-recommend large engagements. If AI is not the right answer, we say so.
POC Sprint	You have a validated use case and want working AI on your real data before committing to production. Or you are evaluating AI vendors and want a comparable POC.	Working proof of concept on your data. Accuracy benchmarks. Latency benchmarks. Architecture documentation. Go/no-go recommendation for production.	4-6 weeks	POC code is production-architected from day one. Path from POC to production is a deployment, not a re-architecture.
End-to-End Project	You have a validated use case and need a partner to build, integrate, deploy, and support it in production. Most common engagement model.	Complete production system: trained models, inference pipeline, integrations, deployment, monitoring, runbook, handover. Full IP ownership. Zero lock-in.	12-20 weeks (16 typical)	Same architect from Discovery through Production. No handoffs. Mitesh stays on the engagement throughout.
Dedicated AI Team	You have an active AI roadmap and need an embedded team (3-8 engineers) for 6-18 months. Or you need to scale a delivery cadence beyond what your in-house team can support.	Dedicated team of AI engineers, ML engineers, and data engineers, with a senior architect on point. Time-zone overlap with US East and EU Central. Full integration with your sprint cadence and tooling.	6-18 months	Same engineers throughout, no rotation, no bench substitution. Replacements only on agreed transition.

Most enterprise engagements start with either an AI Readiness Assessment (for buyers new to AI) or a POC Sprint (for buyers with a validated use case). Roughly 70% of POC Sprints proceed to End-to-End Projects on the same engagement. Dedicated AI Team engagements are typically initiated by buyers who have already shipped one production AI system and are scaling to a second, third, or fourth.

Pricing varies by engagement scope, deployment complexity, and infrastructure prerequisites. Rough ranges and detailed scoping are best discussed in a 30-minute discovery call. Book one →

Still figuring out which service fits?

Thirty minutes with the founder. Or Twenty minutes with the readiness checklist.

Both routes are real. The discovery call is direct: Mitesh on the line, no SDR sequence, no slide deck. The readiness assessment is asynchronous: eight questions, instant scorecard, take it on your own time.

No SDR sequence · 4-hour response commitment · NVIDIA Certified AI Architect on the call

— THE BUYER’S COMPARISON

AI development company comparison: in-house · freelancer · generalist agency · BrainyNeurals

Most enterprise teams figuring out how to evaluate AI development partner choices have four real options: build with an in-house hire, contract a freelancer or marketplace developer, engage a generalist software agency that does AI on the side, or engage a specialist AI firm. Each has legitimate use cases. This AI development company comparison makes the trade-offs explicit so you can pick the right model for your stage and risk appetite.

Concern	In-House Hire	Freelancer	Generalist Agency	BrainyNeurals
Time to first production system	6-12 months (hiring + ramp)	8-16 weeks (variable quality)	6-9 months (handoff overhead)	12-20 weeks (16 typical)
Senior architect involvement	Depends on hire seniority	Usually none	Reserved for largest accounts	On every engagement
Production deployment success rate	High once team is built	Variable; POC-to-production gap is high	Moderate; handoff loses context	High; same team end-to-end
Specialty depth (Edge, CV, Doc AI)	Single specialty per hire	Single specialty per developer	Generalist; AI is one practice of many	AI-only; eight years across all specialties
Compliance & security posture	Built per project	Variable; usually basic	Standardized but generic	ISO 27001 certified · SOC 2 ready architecture
Time-zone overlap with US/EU	In-region	Variable	Variable	Daily standups during EST and CET hours
IP ownership	You own	Contract-dependent	Contract-dependent	You own; full IP transfer at production go-live
Best fit	Long-term roadmap, multi-year AI commitment	Specific tactical task, low-risk	You also need adjacent non-AI work	Production AI within fixed timeline, specialist depth required

The right AI vendor selection criteria depend on what you are actually buying. If you have a multi-year AI roadmap and the talent budget to staff it, build in-house: the long-term economics favor it. If you have a tactical task and limited budget, a marketplace freelancer is the right tool. If your AI work is bundled with web, mobile, or back-office IT modernization, a generalist agency that handles all of it is more efficient than splitting vendors. We are designed for the case where production AI is the primary deliverable, the timeline is fixed, and specialist depth (Edge, CV, Document AI, GenAI agents) matters more than headcount. That is the core AI vendor selection criterion most enterprise teams under-weight in early evaluation.

— TECH STACK

The AI Technology Stack We Ship On.

Our AI technology stack spans five categories: Edge and Hardware, Vision and Video, NLP and Generative AI, MLOps and Inference, and Cloud and Compliance. The named tools below are tools we have deployed in production. Not aspirational additions to a capabilities deck.

Edge & Hardware

NVIDIA Jetson Orin / AGX / Nano · Qualcomm SNPE SDK · Rockchip RK3588 · Kneron KL520/720 · Intel RealSense (D435, D455) · Stereolabs ZED 2/X · Ouster OS-1 lidar · GPS-RTK · ROS / ROS2 · TensorRT optimization for sub-50ms inference budgets.

Vision & Video

YOLO v5/v8/v10 · Detectron2 · MMDetection · Ultralytics · OpenCV · NVIDIA DeepStream · Triton Inference Server · multi-camera fusion pipelines · cross-camera person re-identification · pose estimation (HRNet, OpenPose).

NLP & Generative AI

Claude (Anthropic) · GPT (OpenAI) · Gemini (Google) · Llama 3 / 4 · Mistral · LangGraph · LangChain · CrewAI · pgvector · Pinecone · Weaviate · Qdrant · Milvus · ABBYY · Kofax · Rossum-compatible architectures.

MLOps & Inference

NVIDIA Triton Inference Server · TensorRT · ONNX Runtime · Kubeflow · MLflow · Weights & Biases · DVC for data versioning · feature stores (Feast) · drift monitoring · model retraining pipelines · A/B inference routing.

Cloud & Compliance

AWS (SageMaker, Bedrock, Rekognition, Kinesis Video) · Azure (ML Studio, Cognitive Services, OpenAI Service) · Google Cloud (Vertex AI, Document AI) · ISO 27001 certified · SOC 2 ready architecture · GDPR, HIPAA, PCI DSS-aware system design.

— INDUSTRIES WE SERVE

Where our AI runs in production.

We have shipped production AI systems across five enterprise industries. The depth varies by industry: some are decade-deep practices, others are recent expansions. Pick the industry closest to yours and read the deep page for vertical-specific use cases, compliance posture, and case studies.

Manufacturing & Industrial

Quality inspection, defect detection, worker safety monitoring, predictive maintenance, OEE intelligence on factory-floor edge hardware integrated with SCADA and MES.

See industry page →

BFSI · Banking, Insurance, Financial Services

Document AI for KYC, claims, mortgage, compliance filings. Real-time fraud detection. SOC 2 and PCI DSS-ready architectures.

See industry page →

Healthcare & Life Sciences

Medical imaging analysis, clinical document extraction, pharma manufacturing intelligence, HIPAA-compliant AI systems with full audit logging.

See industry page →

Logistics & Supply Chain

Warehouse safety AI, fleet dashcam analytics, package damage detection, cold chain monitoring, last-mile delivery optimization on rugged edge hardware.

See industry page →

Construction & Infrastructure

Site safety monitoring, progress tracking, BIM-integrated analytics, drone inspection AI, civil-plan automation.

See industry page →

If your industry is not listed, that does not mean we cannot serve it. It means we have not yet shipped a production case study there. Tell us about your problem in a discovery call, and we will be honest about whether our depth fits.

Eight years of doing only this. The depth shows up in week one.

70+

Production AI Systems Delivered

20

Specialist AI Engineers

8 yrs

Exclusively in Applied AI

NVIDIA

Certified AI Architect

— WHY BRAINYNEURALS

Why enterprise buyers pick us over larger generalist firms.

Every reason below is something we can defend in week one of an engagement. Generic claims like “passionate” and “world-class” are absent on purpose.

Specialist firm, not a generalist with an AI page

Twenty engineers, all working exclusively on production AI. We do not staff a web practice, a mobile practice, or a generic IT services bench. The depth in Computer Vision, Edge AI, Document AI, and Generative AI comes from eight years of working only on these problem classes. Generalist firms cannot replicate that without restructuring.

Founder-led architecture review on every engagement

Mitesh Patel (NVIDIA Certified AI Architect, M.Tech Embedded Systems, eight years applied AI) personally leads the architecture review on every engagement. Not a brand asset. Not an advisory role. The architect who designs your system is the architect who stays on the engagement through production.

Production-grade by default

Our POCs are architected for production from day one. Same data pipeline, same inference stack, same monitoring layer. Going from our POC to our production deployment is a deployment exercise, not a re-architecture. That is why our typical timeline is 16 weeks rather than 9-12 months.

Same team end-to-end

Most AI engagements lose context across three handoffs: sales to architecture, architecture to build, build to deployment. We do not have these handoffs. The architect who scopes the engagement is the architect who designs it. The engineering lead who ships the POC is the engineering lead who hardens production. Same team, week one to week 52.

Time-zone overlap with US East and EU Central

Daily standups during US Eastern and Central European business hours. Weekly demo sessions on working functionality, not status reports. Engineering communication discipline: Slack response within 4 hours during business hours, escalation paths for after-hours production issues. The collaboration cadence matters as much as the engineering depth.

Compliance-aware system design from day one

ISO 27001 certified. SOC 2 ready architecture. GDPR, HIPAA, and PCI DSS-aware design across financial services, healthcare, and EU client engagements. Security and compliance are part of architectural decision-making in Phase 01, not an audit step at the end of Phase 03 (which is when most AI projects discover their security debt).

— FEATURED OUTCOMES

What production AI looks like, in three engagements.

Three engagements selected from our case-study library to span service cluster, industry, and engagement model. Read the full case studies for architecture details, decision trade-offs, and the timeline of how the system actually shipped.

Manufacturing

Computer Vision Development

99.2% inspection accuracy at 30 fps

A tier-1 automotive component manufacturer was running manual quality inspection on a high-throughput line. Defect-escape rates were triggering customer chargebacks. We deployed a multi-camera computer vision system on Jetson Orin edge hardware integrated with the existing SCADA. POC in 5 weeks. Production go-live in 14 weeks. Full audit trail logging for ISO 9001 compliance.

Read full case study →

BFSI

Document AI / IDP

97.4% extraction across 14 document types

A retail bank was processing 8,000+ KYC document packages per month with a 12-day average turnaround. We built a Document AI system extracting fields across passport scans, utility bills, bank statements, and PAN cards, with human-in-the-loop fallback for low-confidence extractions. SOC 2 ready architecture, full audit logging. Turnaround compressed from 12 days to 4 hours.

Read full case study →

Construction

Document AI + Computer Vision

70% reduction in civil-plan approval time

A municipal authority was approving civil construction plans with a 90+ day average review cycle, bottlenecked on manual checks against zoning rules, setback requirements, and structural compliance. We built a hybrid AI system combining document understanding for plan annotations and computer vision for drawing-element extraction, with rule-engine integration. Approval cycles dropped from 90+ days to under 30.

Read full case study →

— FAQ

Frequently Asked Questions

What does an enterprise AI services partner actually do?

An enterprise AI services partner architects, builds, and deploys production AI systems for mid-market and enterprise clients. At BrainyNeurals, that means eleven specific services across Vision Intelligence, Language and Generative AI, Strategic AI, and Edge and Hardware AI clusters. We do not sell licensed AI software. We build custom AI systems on your data, integrated into your infrastructure, with full IP transfer at production go-live.

What is the typical AI development timeline for an enterprise engagement?

Our typical end-to-end AI development timeline is 16 weeks from kickoff to production go-live: 2 weeks Discovery, 4 weeks POC, 6 weeks Production hardening, 4 weeks integration and acceptance. Faster engagements (6-10 weeks) are possible for narrower use cases like pure POCs, single-camera CV systems, or contained Document AI deployments. Longer engagements (20-26 weeks) apply when the deployment spans multiple sites, complex compliance frameworks, or hardware procurement.

How is enterprise AI services different from "AI strategy advisory"?

Strategy advisory is the assessment and feasibility layer: readiness, use-case prioritization, technology selection, ROI modeling. Enterprise AI services is the build-and-ship layer: actual production AI systems delivered on your data and infrastructure. We deliver both. Most engagements use the assessment as the entry point and then transition to one of our build-focused engagement models. Generalist advisory firms typically stop at the strategy layer; we go through to production. See the Strategic AI cluster for the assessment side.

How do you compare to larger generalist firms with bigger headcount?

This is exactly the question our Buyer’s Comparison framework above is designed to answer. Larger generalist firms have more total engineers, but fewer engineers exclusively focused on AI (typically 5-8% of headcount). We are 100% AI-focused. The depth in Edge AI, Computer Vision, and Document AI compounds from eight years of working on the same problem classes. For engagements where production AI is the primary deliverable and specialist AI firm depth matters more than headcount, the trade favors the specialist. For engagements where AI is bundled with non-AI work, a generalist is more efficient.

Can we do an assessment before committing to a build engagement?

Yes. Our 2-4 week assessment engagement (part of our Strategic AI cluster) produces a readiness scorecard, a prioritized use-case portfolio, and an explicit go/no-go recommendation. We have given “do not proceed” recommendations to clients ready to invest $300K+ when the underlying data could not support the use case. The assessment can be a standalone engagement, or the entry point to a POC Sprint or End-to-End Project. Talk to our consulting team to scope the right starting point.

What AI engagement model fits a long-term embedded-team need?

The AI engagement model that fits long-term embedded-team work is our Dedicated AI Team option: 3-8 engineers (AI engineers, ML engineers, data engineers, with a senior architect on point) embedded in your engineering organization for 6-18 months. Same engineers throughout. No rotation, no bench substitution. Time-zone overlap with US Eastern and Central European business hours is included by default. Best fit for enterprises with active AI roadmaps that need to scale delivery cadence beyond what an in-house team can support.

What is your AI development methodology and AI development process?

Our AI development methodology runs in four phases: Discovery and Feasibility (2 weeks), POC Development (4 weeks), Production Deployment (6 weeks), and Continuous Optimization (ongoing). Each phase has fixed timing, named deliverables, and an explicit go/no-go gate at its close. The POC code path is the production code path; we do not write throwaway code, which is what makes the 16-week end-to-end timeline real rather than aspirational. The same AI development process runs across every engagement model, scaled to engagement scope.

What is the AI technology stack you build on?

Our AI technology stack spans Edge and Hardware (NVIDIA Jetson, Qualcomm SNPE, Triton, TensorRT), Vision and Video (YOLO, Detectron2, DeepStream), NLP and Generative AI (Claude, GPT, Gemini, Llama, LangGraph, pgvector, Pinecone), MLOps and Inference (Triton, ONNX Runtime, MLflow, Kubeflow), and Cloud and Compliance (AWS, Azure, GCP, ISO 27001, SOC 2-ready). Every named tool is one we have shipped on. Not an aspirational addition.

How do you handle data security and compliance during engagements?

We are ISO 27001 certified. Our architecture is SOC 2 ready and designed with GDPR, HIPAA, and PCI DSS in mind. Security and compliance are part of architectural decision-making in Phase 01, not an audit step at the end of Phase 03. We sign mutual NDAs before discovery and Data Processing Agreements before any client data leaves your environment. Data residency, encryption at rest and in transit, and audit logging are standard.

How do we start an engagement with you?

Two routes. The discovery call is the most direct: 30 minutes with Mitesh Patel on the line, no SDR sequence, no slide deck, working through your specific problem and figuring out which engagement model fits. The AI Readiness Assessment is the asynchronous route: eight questions, instant scorecard, useful for buyers who want to scope internally before booking a call. Most engagements start with one or the other, and roughly 70% of POC Sprints proceed to End-to-End Projects on the same engagement.

- 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