Every question we get asked, answered in full.

Brainy Neurals is an AI-only engineering company that has delivered more than 70 production enterprise AI systems since 2018. It builds custom computer vision, generative AI, edge AI, document AI, RAG, AI agent, and robotics systems, and transfers full ownership of the code and trained models to the client at delivery.

Since 2018. That is roughly eight years of continuous production AI delivery, predating the 2023 wave of companies that formed after ChatGPT made AI mainstream.

Twenty engineers. The bench is deliberately small. All 70-plus delivered engagements came from this team rather than from a larger one.

Mitesh Patel, an NVIDIA Certified AI Architect with a background in electronics, embedded systems, and C and C++ firmware development before moving into AI in 2018.

Yes. The company holds ISO 27001 certification for information security management. Mitesh Patel holds the NVIDIA Certified AI Architect credential. The company is an NVIDIA Inception Partner and participates in AWS Activate and Microsoft for Startups.

The company holds ISO 27001 certification, is an NVIDIA Inception Partner, and has independently verifiable presence on Clutch and Upwork, where the founder holds Top Rated Plus status. All three major AI infrastructure providers, NVIDIA, AWS, and Microsoft, have independently validated its engineering through their partner programmes.

Brainy Neurals operates as a distributed engineering company serving clients across the United States and Europe, with a US presence in Illinois. Delivery runs on overlapping EST and GMT business hours with daily standups and weekly demonstrations. The registered legal entity is Brainy Neurals Private Limited.

Yes. The primary client concentration is the United States and Western Europe, but engagements are accepted globally where time zone overlap allows for the daily communication cadence the delivery model depends on.

Mid-market and enterprise organisations, typically 50 employees and above, where the buyer is a CTO, VP of Engineering, Head of AI, or an operations leader with budget authority. Smaller organisations are served through the POC Sprint and advisory models.

Yes. Named project references are provided with candidate CVs during the shortlist stage of an engagement, and reviewed engagements are published on Clutch. Case study detail is available under NDA where the client has not authorised public disclosure.

Three specifics rather than claims. AI is the only thing the company builds, so there is no general software practice subsidising an AI side line. The founder personally architects every engagement, so the person who scopes the work is accountable for the system that ships. Rate bands, project ranges, and delivery timelines are published rather than gated behind a sales call.

Yes, and it is part of the discovery process. If the data is not there, the use case is not viable, or another approach would serve the client better, that is said on the discovery call rather than after a paid scoping phase.

Eleven service lines: AI consulting and strategy, AI POC and MVP development, computer vision development, video analytics, generative AI development, RAG development, AI agent and copilot development, document AI, edge AI and embedded development, robotics AI, and MLOps services.

Computer vision and edge AI. Those are the areas with the most delivered engagements and the deepest founder specialism, rooted in a firmware and embedded systems background that converts directly to on-device inference work.

Yes. Object detection, segmentation, multi-object tracking with re-identification, classification, depth sensing, LiDAR and point cloud processing, and OCR. Applications include quality inspection at line speed, safety monitoring, dimensional measurement, counting, and drawing interpretation.

Yes. Enterprise LLM applications, fine-tuning, prompt architecture, evaluation harnesses, and guardrail design, alongside RAG systems and autonomous agents.

A standard setup is top-k vector search over an embedding index. Brainy Neurals delivers hybrid retrieval combining vector and keyword search, cross-encoder re-ranking, custom or fine-tuned embedding models, metadata filtering, document-level access control that respects existing permissions, version-controlled knowledge bases with effective dates, stale content detection, and retrieval audit logging.

Yes, and it is a core specialism. Deployment targets include NVIDIA Jetson Nano, Orin, and AGX, Qualcomm SNPE, Kneron, Rockchip, Intel OpenVINO, Coral TPU, and Hailo. Optimisation through INT8 and FP16 quantisation, pruning, distillation, and layer fusion typically achieves 3x to 10x speedup with under 1 percent accuracy loss.

Yes. Systems work with any camera supporting ONVIF, RTSP, or major proprietary protocols including Hikvision, Dahua, Axis, Bosch, and Hanwha. Camera replacement is typically not required. Unusual or very old cameras are verified for compatibility during scoping.

The company builds AI agents and copilots rather than scripted chatbots. The distinction is that an agent evaluates a request and resolves it end to end across enterprise systems, with tool access controls and human escalation, rather than following a decision tree.

Yes. Integrations are built through APIs to CRM, ERP, ITSM, EHR, MES, SCADA, and legacy systems rather than through screen scraping. Integration architecture is defined during the scoping phase and forms part of the fixed scope.

Annotation is handled as part of delivery using CVAT, Label Studio, Roboflow, V7, and active learning pipelines. Annotation at volume is a separate operation and is scoped independently.

Yes. Where real data is scarce, synthetic data generation through NVIDIA Omniverse, Blender with domain randomisation, and Unity Perception is used, typically combined with a smaller real dataset rather than replacing it.

That is one of the most common findings on a discovery call, and it is answered before a build is quoted. Options include transfer learning from pretrained models, active learning to prioritise the highest-value labelling effort, synthetic data generation, and staged collection during a pilot. If the data genuinely is not there and cannot be collected in a reasonable window, the honest answer is that the project is not yet viable, and that is what is said.

Optionally. Ongoing engagement covers retraining, monitoring, drift detection, and expansion. It is optional rather than mandatory, because the client owns the full codebase and can maintain it internally or through another vendor.

Hourly rates run from $55 to $180 depending on role and seniority. Fixed-price proofs of concept run $15,000 to $60,000, MVPs $60,000 to $150,000, and production hardening $40,000 to $120,000. Dedicated teams are priced as a monthly retainer, from $40,000 to $55,000 for four engineers up to $95,000 to $135,000 for ten.

Between $55 and $130 per hour depending on seniority. Junior developers with under 3 years of production experience bill at $55 to $75, mid-level developers with 3 to 6 years at $65 to $95, and senior engineers with 6 or more years at $85 to $130. AI solution architects bill at $110 to $180 and are usually engaged 4 to 20 hours per week alongside a delivery team.

$15,000 to $60,000 over 4 to 8 weeks, priced as a fixed fee. The POC uses the client’s real production data and ends in a documented go, not yet, or no verdict measured against thresholds agreed at the start.

Because the available talent pool for those skills is shallow across the entire industry, not because the work is billed differently. Both require production experience that cannot be substituted with general ML familiarity.

No. Published rates are all-inclusive of engineering labour and project management, with no separate charge for tooling, project management, security overhead, or engineer swaps. Excluded and paid directly by the client at cost: GPU infrastructure, third-party model API charges, and specialised hardware such as cameras and sensors. Indicative infrastructure spend is $2,000 to $15,000 per month on a typical engagement.

No. There are no per-seat fees, no per-query fees, and no per-camera monthly licensing on custom builds. Pricing is one-time development plus optional ongoing support.

A five-engineer AI team costs $1.4M to $1.8M fully loaded over 12 months as in-house US hires, against $650,000 to $960,000 through Brainy Neurals. Modelled saving is approximately $780,000 including recruitment fees, taxes, benefits, equipment, and ramp-time opportunity cost. The larger difference is often time: 4 to 7 months to a productive in-house senior AI hire against 14 days.

Consulting firm rate cards for senior AI engineers run $220 to $400 per hour against $85 to $130 at Brainy Neurals. Over a 12-month five-engineer engagement that is $2.4M to $4.2M against $650,000 to $960,000.

Not always on headline rate, and that is not the comparison that matters. Marketplace rates range $80 to $200 with highly variable quality, no compliance posture, and the client carrying all delivery risk. Brainy Neurals carries delivery risk contractually, holds ISO 27001, and replaces an underperforming engineer within 5 business days at no cost.

One month, or 160 hours, for staff augmentation. Three months for a dedicated team contract. Per statement of work for fixed-price projects. Most first-time clients start with a two-week paid trial of one to three engineers, costing between roughly $4,400 and $19,200.

Yes, a two-week paid trial on every new engagement. If the client converts within 30 days of trial end, 50 percent of the trial cost is credited against the first invoice. If they do not proceed, delivered work remains their property with no termination fee.

Fixed-price projects are milestone-paid. Dedicated teams are billed as a monthly retainer. Staff augmentation is hourly with monthly invoicing.

Rate bands are published and applied consistently. Scope, team composition, and engagement duration are where the commercial conversation actually happens, because those are the variables that move total cost.

Four: End-to-End Project, POC Sprint, Dedicated AI Team, and AI Readiness Assessment. A two-week paid trial is available on any of them.

It is determined by two variables. How clearly you can already specify what needs to be built, and how much AI engineering capability already exists on your team. High clarity with no internal team points to a End-to-End Project. Low clarity with no internal team points to a POC Sprint. Low clarity with a capable team points to advisory. High clarity with a capable team that lacks specific depth points to an embedded pod.

A 4 to 6 week fixed-fee engagement that validates whether AI can solve the problem on the client’s real data before a full build is committed. It uses production data under production-representative conditions and measures against the client’s own accuracy and latency thresholds. It ends in a go, not yet, or no verdict backed by evidence, and the client owns the prototype and all findings regardless of the verdict.

The client owns the prototype, the benchmarks, and every finding, and has an evidence-backed answer that prevents a much larger wasted investment. That is the intended outcome of a pilot that should not proceed, and it is why the pilot exists.

Yes, and it is usually recommended. A two-week paid trial or a POC Sprint is the standard entry point for a first engagement.

One month for staff augmentation, three months for a dedicated team, per SOW for fixed-price work, and no minimum for advisory.

They are replaced within 5 business days at no cost to the client.

On fixed-price work, Brainy Neurals carries delivery risk. Acceptance criteria are written into the statement of work, and a failed milestone triggers rework at Brainy Neurals’ cost.

Yes, subject to the contract minimum for the model chosen. Work delivered to that point remains client property under the standard IP terms.

A delivery lead on dedicated team engagements, with the client’s own engineering manager managing embedded engineers directly on staff augmentation. Mitesh Patel holds architectural accountability across every model and joins the monthly business review on dedicated team engagements.

Yes. An NDA is executed alongside the MSA and SOW, typically on day 6 of the standard 14-day engagement start process, and often earlier if the client requires one before the discovery call.

A proof of concept runs 4 to 8 weeks. A production build runs 6 to 14 weeks after the POC. A End-to-End Project runs 6 to 12 weeks from scope sign-off to production. POC Sprints run 4 to 6 weeks.

Fourteen calendar days from the first discovery call to engineers actively working, including candidate shortlisting, client-led interviews, contract execution, and tool access provisioning.

A free 30-minute call run personally by Mitesh Patel. It maps the use case, constraints, existing team, and success criteria, and ends with a yes or no from both sides on whether to scope further.

A paid architecture and scoping phase. It produces a data audit, an architecture selection, written acceptance criteria, and a fixed price. Fixed-price work cannot be quoted responsibly without it.

Daily standups during EST or GMT hours, weekly demonstrations of working functionality rather than status reports, a dedicated communication channel, and response times under four hours during business hours. Dedicated team engagements add a monthly business review with the founder.

Working functionality. The delivery model is built around demonstrating the system rather than reporting on it, because a report can describe progress that a demonstration would not survive.

Because the POC is built on the architecture that will scale, using the client’s real production data, under production-representative conditions. A POC built to impress on curated data creates a rebuild problem at production time. Roughly 70 percent of POC Sprints proceed to an End-to-End Project.

That depends on the use case, but the sequencing is deliberate. The POC produces measured accuracy against your thresholds within 4 to 8 weeks, which is the first evidence point. Business results follow production deployment and the operational change around it.

Named engineers from a 20-person bench, shortlisted 2 to 3 per role with CVs and named project references, and interviewed by the client before selection. A junior engineer never leads a production AI deployment. That is policy.

Computer vision engineers, generative AI and LLM developers, MLOps engineers, NLP engineers, edge and embedded AI engineers, AI solution architects, AI-specific data engineers, and AI agent and copilot developers.

Yes. Client-led interviews occur on days 3 to 5 of the standard engagement start process, before selection.

Mitesh Patel runs every discovery call personally, architects every engagement, and joins the monthly business review on dedicated team engagements. There is no SDR layer between an enquiry and an engineer.

Delivery runs on overlapping EST and GMT business hours with daily standups. Embedded engineers work inside the client’s own workflow and tools, including Jira, GitHub, and Slack.

Yes on staff augmentation and embedded pod models. Engineers work inside the client’s repository, ticketing system, and communication channels rather than a parallel vendor stack.

PyTorch is primary, with TensorFlow, JAX, and ONNX also in production use. Vision work uses YOLO, Detectron2, MMDetection, SAM2, DINOv2, and Grounding-DINO. Serving runs on NVIDIA Triton, TensorRT, vLLM, KServe, and BentoML.

GPT-4o, Claude Sonnet and Opus, Gemini, Llama 3.3 in 8B and 70B, Mistral Large, Qwen, and DeepSeek. Model selection is benchmarked against the client’s actual task rather than defaulted to a preferred vendor.

No. Architecture is built so the model layer can be swapped, and provider selection is a benchmarked decision rather than a fixed dependency.

It depends on the physics of the environment. A cloud model with 200ms round-trip latency cannot reject a defective part on a conveyor moving at 2 metres per second, because the part has travelled 40cm past the inspection point before the result returns. Edge deployment suits latency-critical, bandwidth-constrained, or sovereignty-constrained cases. Cloud suits high-volume batch work where latency tolerance is higher. Most enterprise deployments end up hybrid, with edge handling real-time decisions and cloud handling retraining, monitoring, and fleet management.

Production edge systems process 30 or more frames per second on NVIDIA Jetson Orin with multiple concurrent detection models running simultaneously. Optimisation through TensorRT quantisation, pruning, and layer fusion typically delivers 3x to 10x speedup with under 1 percent accuracy loss.

Selection depends on scale, filtering requirements, and existing infrastructure. Production deployments have used Pinecone, Weaviate, Qdrant, Milvus, Chroma, and Postgres pgvector.

Yes. Full on-premises deployment is supported, including air-gapped deployment for high-security environments.

Through monitoring and retraining pipelines built as part of the delivery rather than added afterwards. Tooling includes Evidently AI, Arize, Fiddler, WhyLabs, and Prometheus with Grafana. Drift detection and a retraining trigger are part of the MLOps scope on production engagements.

Against thresholds the client sets before work begins, measured on the client’s own data under production-representative conditions. Accuracy claims are stated as measured figures, such as 99.2 percent, rather than as descriptions like high accuracy.

Through RAG grounding against source documents, structured output enforcement, confidence scoring with escalation on low-confidence responses, evaluation harnesses that measure hallucination rate as a tracked metric, and output validation before any action executes in agentic systems.

Fallback behaviour is designed into the architecture rather than discovered in production. That includes provider failover where the task allows it, degraded-mode operation, and queuing with retry for asynchronous work.

Yes, for information security management.

AES-256 encryption at rest, TLS 1.2 or higher in transit, keys managed through enterprise key management services or HSM-backed for on-premises, SSO through SAML 2.0 or OIDC, MFA for administrative access, role-based access control, and audit logging of all user and system actions.

Processing region is configurable per deployment. EU deployments can be constrained to EU regions. Full on-premises deployment is available where data sovereignty requirements demand it.

No. Client data remains client property and is processed only as necessary to deliver the engaged service.

Yes. A business associate agreement is executed before any protected health information becomes accessible. De-identification pipelines cover all 18 HIPAA identifiers using the Safe Harbor method, with Expert Determination available for limited datasets. Every PHI access, by human or by model, is logged with timestamp, identity, action, and data accessed.

Systems are designed for GDPR from the architecture stage. That includes data residency controls, mechanisms supporting data subject access requests, and redaction workflows where imagery contains multiple people.

Brainy Neurals holds ISO 27001 certification and designs systems to be SOC 2 aligned, including the audit logging and access control that SOC 2 examination requires. Clients requiring a SOC 2 report from their vendor should raise it during scoping.

All client data is deleted from Brainy Neurals systems within contractually defined timeframes, including backups, archival copies, and derived metadata.

Through tool access controls scoping each agent to specific authorised actions, input sanitisation against prompt injection, output validation before any action executes, reasoning trace logging for auditability, confidence thresholds that trigger human escalation, and rollback for reversible actions.

Yes, annually by an independent third party, alongside continuous vulnerability scanning. Critical patches are applied within 14 days of disclosure and high severity within 30 days.

The client owns 100 percent of it. That covers source code, trained model weights, training scripts, data pipelines, evaluation suites, configuration, and documentation.

Yes, including the weights and the training scripts that produced them.

No. The client can operate, modify, extend, or transfer the delivered system to another team or vendor. There is no per-seat, per-query, or per-camera licensing on custom builds.

That is an anticipated outcome rather than a problem. The full codebase, models, and documentation are already the client’s property, and handover documentation and operational runbooks are part of delivery.

Brainy Neurals, on fixed-price work. Acceptance criteria sit in the statement of work and a failed milestone triggers rework at Brainy Neurals’ cost.

Yes. Quality inspection at line speed, defect detection, predictive maintenance, assembly verification, and process optimisation, with integration into SCADA, MES, and PLC systems. A delivered tire defect detection system runs at 99.2 percent accuracy at over 200 units per hour with sub-50ms reject decisions.

Yes. Medical imaging, clinical NLP, coding automation, and clinical trial document processing, under HIPAA-aligned architecture with executed business associate agreements. A delivered medical coding system reduced turnaround from 48 hours to 4 hours.

Yes. Document AI for KYC and compliance review, fraud detection, risk analytics, and retrieval over policy and regulatory documents, with audit trails and document-level access control.

Yes. Drawing and site plan interpretation, permit and plan review automation, progress tracking, and site safety monitoring. A delivered civil plan review system reduced approval time from 3 weeks to 4 days.

Yes. Warehouse and yard vision, loading and throughput analytics, damage detection, inventory counting, and demand forecasting.

The underlying capabilities transfer across sectors. What changes is the domain constraint set, and that is what the discovery call and scoping phase establish. If the fit is poor, that will be said directly. The full sector list sits on the industries hub.

Email hello@brainyneurals.com or book a 30-minute architecture call directly at tidycal.com/mitesh-ai-consultant. The contact page lists every route.

Within one business day. The first reply comes from an engineer or from Mitesh Patel rather than from a sales development representative.

The problem you are trying to solve, what data you already have and where it lives, any accuracy or latency threshold the system would have to hit to be useful, your current team composition, and your timeline. None of it needs to be formal. The call exists to establish whether the work is viable.

Yes. The 30-minute discovery call is free and is run by Mitesh Patel personally.

No. A large share of engagements begin with a problem rather than a specification, and the AI Readiness Assessment exists precisely for the case where the specification cannot yet be written.

Discovery call, then a paid two-week trial or a 4 to 6 week POC Sprint. Engineers are actively working within 14 calendar days of the first call.