Services Enterprise AI

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.

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NVIDIA Inception Partner · AWS Activate · Microsoft for Startups · ISO 27001 Certified · Upwork Top Rated Plus

40+ AI Projects
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10+ Years Pure
AI Focus
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Trusted by teams across USA, Europe & Asia
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Founded by Mitesh Patel — NVIDIA Certified AI Architect
Founder · technical authority
Founded by Mitesh Patel — NVIDIA Certified AI Architect
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The Specialist Thesis

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.

Argument 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. 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.

Argument 02

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.

Argument 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. That is why our typical timeline from discovery to production is 16 weeks rather than the 9-12 months that POC-graveyard projects average.

Argument 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. 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.

Certifications & cloud ecosystem
The Service Map

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.

Cluster 01 / 04

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.

01 / 11

Computer Vision Development

★ Core Expertise

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.

02 / 11

Video Analytics & Intelligent Surveillance

Real-time pipelines

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.

Cluster 02 / 04

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.

04 / 11

Document AI · IDP

★ Core Expertise

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.

05 / 11

Generative AI Development

Model-agnostic

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.

06 / 11

RAG (Retrieval-Augmented Generation)

Grounded answers

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.

07 / 11

AI Agent & Copilot Development

Multi-step reasoning

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.

Cluster 03 / 04

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.

08 / 11

AI Consulting & Strategy

Engineer-led advisory

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.

09 / 11

AI POC & MVP Development

4–6 weeks

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.

Cluster 04 / 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.

10 / 11

Edge AI and Embedded AI Development

★ Core Expertise

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.

11 / 11

Robotics and Hardware Automation

ROS / ROS2

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.

Cross-cluster engagements

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.

Our 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 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.

Weeks 1–2 · P2W
Discovery and Feasibility
Weeks 3–6 · P4W
POC Development
Weeks 7–12 · P6W
Production Deployment
Ongoing
Continuous Optimization
Phase 01 / 04

Discovery and Feasibility

Weeks 1–2 · P2W

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 02 / 04

POC Development

Weeks 3–6 · P4W

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 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.

Phase 03 / 04

Production Deployment

Weeks 7–12 · P6W

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 1.

Phase 04 / 04

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 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
Go / No-go Gate

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.

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
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.
Selection guidance

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
Book a 30-Minute Discovery Call Take the AI Readiness Assessment
The Buyer's Comparison

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
↔ Scroll horizontally to compare all four columns
Interpretation

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 & Capabilities

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.

Stack 01 / 05

Edge & Hardware

Jetson Orin Jetson Orin Jetson AGX Logo Jetson AGX Jetson Nano Qualcomm SNPE SDK Qualcomm SNPE SDK Rockchip RK3588 Rockchip RK3588 Kneron KL520/720 Kneron KL520/720 Intel RealSense D435/D455 Intel RealSense D435/D455 Stereolabs ZED 2/X Stereolabs ZED 2/X Ouster OS-1 lidar GPS-RTK ROS / ROS2 TensorRT Logo TensorRT (sub-50ms)
Stack 02 / 05

Vision & Video

YOLO Icon YOLO v5/v8/v10 Detectron Icon Detectron2 MMDetection Ultralytics OpenCV Logo OpenCV NVIDIA DeepStream Triton Inference Server Triton Inference Server Multi-camera fusion Cross-camera Re-ID HRNet / OpenPose
Stack 03 / 05

NLP & Generative AI

Claude (Anthropic) Claude (Anthropic) GPT (OpenAI) GPT (OpenAI) Gemini (Google) Gemini (Google) Llama 3 / 4 Llama 3 / 4 Mistral Mistral LangGraph LangChain LangChain CrewAI pgvector Pinecone Pinecone Weaviate Weaviate Qdrant Qdrant Milvus Milvus ABBYY Kofax Rossum-compatible
Stack 04 / 05

MLOps & Inference

NVIDIA Inception Program NVIDIA Triton TensorRT Logo TensorRT ONNX Runtime ONNX Runtime Kubeflow MLflow MLflow Weights & Biases DVC Feast (feature store) Drift monitoring A/B inference routing
Stack 05 / 05

Cloud & Compliance

AWS Logo AWS SageMaker AWS Bedrock AWS Rekognition Kinesis Video Azure ML Logo Azure ML Studio Azure OpenAI Service GCP Vertex Icon Vertex AI Google Document AI ISO 27001 certified SOC 2 ready GDPR / HIPAA / PCI DSS
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.

Vertical 01 / 05

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 page
Vertical 02 / 05

BFSI · 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 page
Vertical 03 / 05

Healthcare & Life Sciences

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

See industry page
Vertical 04 / 05

Logistics & 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 page
Vertical 05 / 05

Construction & Infrastructure

Site safety monitoring, progress tracking, BIM-integrated analytics, drone inspection AI, and 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.

The Track Record
0+
AI Projects
Delivered
0+
Specialist AI
Engineers
0+Years
AI Experience
Applied AI
NVIDIA
Certified
AI Architect,
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.

· 30 minutes · Mitesh Patel on the line · No SDR sequence · 4-hour response commitment
Talk to the Architect
Why Brainy Neurals

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.

Reason 01 / 06

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.

Reason 02 / 06

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.

Reason 03 / 06

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.

Reason 04 / 06

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.

Reason 05 / 06

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.

Reason 06 / 06

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).

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
Tier-1 Manufacturer · Defect Detection

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.

Case 01 / 03 Read full case study
BFSI Document AI / IDP
Banking · Document AI for KYC

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.

Case 02 / 03 Read full case study
Construction Document AI + Computer Vision
Construction · Civil Plan Automation

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.

Case 03 / 03 Read full case study
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.
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.
Near the end of the page? Pick your next move.

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.

Computer Vision Development Edge AI & Embedded Development Document AI / IDP Generative AI Development AI POC & MVP Development
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· NVIDIA Certified AI Architect on the call · 4-hour response commitment · No SDR sequence · No slide deck