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 firms in this category are generalists with an AI page. We are not. Every engineer here works exclusively on production AI, and we have been doing only this since 2018. From a 4-week proof of concept to a 16-week production rollout, we architect the system, build it on your real data, and integrate it into the infrastructure you already operate. Same team from discovery through production go-live. This is what an enterprise AI partner looks like.
NVIDIA Inception Partner · AWS Activate · Microsoft for Startups · ISO 27001 Certified · Upwork Top Rated Plus
Systems Delivered
Engineers
Applied AI
Certified
Founder-Led
Credentials and clients
NVIDIA Inception Partner
AWS Activate Startup
Microsoft for Startups
ISO 27001 Certified
NVIDIA Certified AI Architect
Upwork Top Rated Plus
Enterprise AI is what we do. It is the only thing 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.
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. The same pattern holds across Computer Vision, Document AI, and Generative AI. Eight years of working on the same problem class compounds. One-off project teams cannot replicate that.
Founder-led, not founder-marketed
Mitesh Patel (NVIDIA Certified AI Architect, M.Tech Embedded Systems, eight years in 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. Larger firms cannot do this; their architects are reserved for the largest accounts.
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. That is why our typical timeline from discovery to production is 16 weeks rather than the 9-12 months that POC-graveyard projects average.
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. We do not have these handoffs. The architect who scopes the engagement is the architect who designs it. The engineering lead who builds the POC is the engineering lead who hardens production. Mitesh stays on the engagement throughout.
Eleven 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.
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.
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 · IDP
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.
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.
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.
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.
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.
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 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 and Feasibility
Weeks 1–2 · P2WWhat 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
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.
POC Development
Weeks 3–6 · P4WWhat 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
End of week 6, we present the POC against the success criteria defined in Phase 1. 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.
Production Deployment
Weeks 7–12 · P6WWhat 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
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 1.
Continuous Optimization
OngoingWhat 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 4 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
Leave completely empty
Sixteen weeks from kickoff to production, then ongoing. Discovery to production with no handoff, no re-architecture, no team change. That is what lets our case-study timelines hold up.
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 |
|---|---|---|---|---|
| Model 01 / 04AI 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 for your situation, we say so. |
| Model 02 / 04POC 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. The path from our POC to our production system is a deployment, not a re-architecture. |
| Model 03 / 04End-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. |
| Model 04 / 04Dedicated AI Team | You have an active AI roadmap and need an embedded team, typically 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 business hours. Full integration with your sprint cadence and tooling. | 6–18 months | Same engineers throughout the engagement, 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
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.
AI development company comparison: in-house · freelancer · generalist agency · BrainyNeurals
Most enterprise teams figuring out how to evaluate an AI development partner 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 | Specialist firmBrainyNeurals |
|---|---|---|---|---|
| 01 / 08Time to first production system | 6–12 months (hiring + ramp) | 8–16 weeks (variable quality) | 6–9 months (handoff overhead) | 12–20 weeks (16 typical) |
| 02 / 08Senior architect involvement | Depends on hire seniority | Usually none | Reserved for largest accounts | On every engagement |
| 03 / 08Production 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 |
| 04 / 08Specialty 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 |
| 05 / 08Compliance & security posture | Built per project | Variable, usually basic | Standardized but generic | ISO 27001 certified · SOC 2 ready architecture |
| 06 / 08Time-zone overlap with US/EU | In-region | Variable | Variable | Daily standups during EST and CET hours |
| 07 / 08IP ownership | You own | Contract-dependent | Contract-dependent | You own, full IP transfer at production go-live |
| 08 / 08Best 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.
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
Jetson Orin
Jetson AGX
Jetson 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 (sub-50ms)
Vision & Video
YOLO v5/v8/v10
Detectron2
MMDetection
Ultralytics
OpenCV
NVIDIA DeepStream
Triton Inference Server
Multi-camera fusion
Cross-camera Re-ID
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
MLOps & Inference
NVIDIA Triton
TensorRT
ONNX Runtime
Kubeflow
MLflow
Weights & Biases
DVC
Feast (feature store)
Drift monitoring
A/B inference routing
Cloud & Compliance
AWS SageMaker
AWS Bedrock
AWS Rekognition
Kinesis Video
Azure ML Studio
Azure OpenAI Service
Vertex AI
Google Document AI
ISO 27001 certified
SOC 2 ready
GDPR / HIPAA / PCI DSS
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, and OEE intelligence, running on factory-floor edge hardware integrated with SCADA and MES.
See industry pageBFSI · Banking, Insurance, Financial Services
Document AI for KYC, claims, mortgage, and compliance filings. Real-time fraud detection. Compliance automation with full audit trail. SOC 2 and PCI DSS-ready architectures.
See industry pageHealthcare & Life Sciences
Medical imaging analysis, clinical document extraction, pharma manufacturing intelligence, and HIPAA-compliant AI systems with full audit logging.
See industry pageLogistics & Supply Chain
Warehouse safety AI, fleet dashcam analytics, package damage detection, cold chain monitoring, and last-mile delivery optimization on rugged edge hardware.
See industry pageConstruction & Infrastructure
Site safety monitoring, progress tracking, BIM-integrated analytics, drone inspection AI, and civil-plan automation.
See industry pageIf 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.
Systems Delivered
Engineers
Applied AI
Certified
Founder-Led
Eight years of doing only this. The depth shows up in week one.
Most AI vendors hand you a salesperson on the first call. We hand you the architect who will design your system. If we proceed, that same architect stays on the engagement through production go-live.
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 from discovery to production is 16 weeks rather than the 9-12 months that POC-graveyard projects average.
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 1, not an audit step at the end of Phase 3 (which is when most AI projects discover their security debt).
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.
99.2% inspection accuracy at 30 fps line speed
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.
97.4% field-level extraction accuracy 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, and a hybrid retrieval pipeline for compliance-sensitive document classes. Turnaround compressed from 12 days to 4 hours.
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 for compliance checks. Approval cycles dropped from 90+ days to under 30.
Pick the right service. Or talk to the architect.
Five fastest routes for the most common buyer paths. Or skip the navigation entirely. Book the discovery call and we will route to the right service together.