auto_awesome AI/ML Consulting from India

    AI/ML Consulting Services in India
    From Strategy to Production

    Agentic AI, retrieval-augmented generation, MLOps, computer vision and predictive maintenance — built and shipped by hjLabs.in for 36+ enterprise clients across 15+ countries.

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    HJ
    By Hemang Joshi — Founder, hjLabs.in
    MSc Electronics · 8+ years industrial automation & AI/ML · Last updated
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    hjLabs.in delivers production-ready AI/ML services to manufacturers and B2B teams across 15+ countries. From a Gandhinagar office, we build agentic AI workflows, retrieval-augmented generation (RAG) systems, fine-tuned LLMs, computer vision pipelines, predictive-maintenance models, and MLOps platforms — typically in 6 to 12 weeks. Every project is led by Hemang Joshi (MSc Electronics, 8+ years industrial automation) and hosted on the client's own cloud.

    Services at a glance

    Why hjLabs.in for AI/ML Consulting

    Most AI consultancies sell you a slide deck. We ship working systems. hjLabs.in is headquartered in Gandhinagar, Gujarat with a senior engineering bar, and our blended rate is roughly 40 to 60 percent lower than comparable US or UK firms — without offshoring the design work to junior staff. Every engagement is led by the founder or a principal engineer, never a sales handler.

    We have delivered AI/ML and automation work for 36+ enterprise clients across 15+ countries, including manufacturers, healthcare providers, agritech operators, logistics companies and security integrators. We are an unusual consultancy in that we also build and ship our own automation hardware and SaaS — proof we know how to take real systems from prototype to production, not just write reports about them.

    Our founder, Hemang Joshi, holds an MSc in Electronics and has spent eight years bridging industrial automation, embedded systems and modern AI. That mix matters: AI projects fail at the integration boundary, where the model meets the PLC, the ERP and the legacy database. We have lived in that boundary for a decade.

    If you have read this far you already suspect most AI vendors are vague on outcomes and aggressive on retainers. We are the opposite — fixed-price scopes, written deliverables, IP assigned to you on payment, and an honest "this is not a fit" conversation when the use case does not justify the spend.

    36+
    Enterprise clients
    15+
    Countries served
    8+ yrs
    Founder experience
    100%
    IP assigned to client
    In-house product team. We sell our own automation hardware (SensorSync IoT) and trading platforms — proof we deploy real systems, not just slides.

    Our AI/ML Services Catalog

    Six core practice areas — each a deep specialty, not a checkbox. Click into any service for methodology, pricing tiers and case studies.

    smart_toy

    Agentic AI Development

    Autonomous LLM agents with tool use, planning and multi-step execution — built on LangGraph, AutoGen, CrewAI and Claude.

    • Reduce L1/L2 ticket handling time 60-80%
    • Automate supply-chain reconciliation end-to-end
    • Eval-first design with NeMo Guardrails baked in
    Explore Agentic AI →
    search

    RAG System Development

    Retrieval-augmented generation over your documents — Pinecone, Weaviate, Qdrant — with hybrid search, reranking and citations.

    • Cut hallucinations by ~90% vs bare LLM
    • 10x faster knowledge retrieval for ops teams
    • Source citations on every answer
    Explore RAG Systems →
    rocket_launch

    MLOps & Deployment

    From notebook to production — CI/CD pipelines, model registries, drift detection and auto-scaling on AWS, GCP or Azure.

    • Ship models 5-10x faster with reusable pipelines
    • Catch silent regressions before they ship
    • SLO-backed inference with full observability
    Explore MLOps →
    model_training

    LLM Fine-tuning

    Domain-tuned LLMs using LoRA, QLoRA, DPO and RLHF on Llama, Qwen and Mistral — with eval suites that prove the lift.

    • Beat generic GPT-4o on your task by 20-40%
    • Cut inference cost 60-80% via small specialized models
    • Self-hosted on your VPC for data sensitivity
    Explore Fine-tuning →
    visibility

    Computer Vision

    Defect detection, OCR, ANPR, surveillance analytics and visual QA — YOLO, SAM, Detectron2 and edge deployment to Jetson and Coral.

    • 30-70% defect-detection accuracy lift on shop floors
    • Real-time inference under 50ms on edge devices
    • Active-learning loops to keep accuracy rising
    Explore Computer Vision →
    build

    Predictive Maintenance

    Vibration, thermal and current signature analysis on rotating machinery — XGBoost, LSTM and transformer-based anomaly models.

    • Reduce unplanned downtime 30-50%
    • Edge-first sensor stack via SensorSync IoT
    • Failure mode classification, not just anomaly flags
    Explore Predictive Maintenance →

    Tech Stack We Work With

    Tools are means, not ends — but the right toolchain shaves weeks off delivery and keeps total cost of ownership predictable. We are model-agnostic and choose the cheapest stack that meets the eval bar, not the trendiest. Below is the stack we currently default to in 2026, refreshed every quarter.

    Agent & LLM Frameworks

    LangGraph, AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Pydantic AI, Semantic Kernel.

    Foundation Models

    Anthropic Claude (Opus, Sonnet, Haiku), OpenAI GPT-4o and o-series, Google Gemini, Meta Llama 3.x, Mistral, Qwen 2.5.

    Vector & Search

    Pinecone, Weaviate, Qdrant, pgvector, OpenSearch, Elastic, ColBERT/ColPali rerankers.

    MLOps & Training

    MLflow, Weights & Biases, Vertex AI, Amazon SageMaker, Kubeflow, BentoML, Modal, RunPod, vLLM.

    Observability & Eval

    LangSmith, Langfuse, Arize Phoenix, Helicone, NeMo Guardrails, Guardrails AI, Ragas, DeepEval.

    Edge & Vision

    NVIDIA Jetson Orin, Google Coral, Hailo, OpenCV, YOLOv8/v11, Segment Anything, Detectron2, ONNX Runtime, TensorRT.

    Industries We Serve

    Vertical experience matters — these are the sectors where we have shipped production AI/ML and have references on file.

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    Manufacturing

    Predictive maintenance reducing unplanned downtime 30-50%, vision-based defect detection on assembly lines, OEE forecasting from MES data.
    agriculture

    Agriculture

    Crop disease detection from drone imagery, yield forecasting, irrigation optimization with soil sensors and weather data.
    local_hospital

    Healthcare

    Clinical-document RAG for prior auth and claims, radiology pre-screening models, HIPAA-aligned deployments on private clouds.
    local_shipping

    Transportation & Logistics

    Route optimization with live traffic, ANPR for fleet gates, demand-forecasting agents that reorder ahead of stockouts.
    security

    Security & Surveillance

    Intrusion detection, weapon detection, PPE compliance and forensic video search across multi-camera deployments.
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    Read full case studies →

    Detailed write-ups with architecture diagrams, eval scores, timelines and references from named clients.

    Engagement Model & Pricing

    Three transparent tiers — pick the smallest one that derisks your decision. We will tell you which tier fits, even when it is the smaller invoice.

    Tier 1

    Pilot Sprint

    $3,000 – $7,000
    2–4 weeks · single use case

    Prove a single use case is feasible before you commit serious budget. We pick one workflow, build a working prototype against your data, run a clean eval, and deliver a written feasibility report with go/no-go recommendation. Most clients use this to derisk a board discussion.

    • Working demo on your data
    • Eval suite with baseline scores
    • Architecture & cost forecast
    Start a pilot
    MOST POPULAR
    Tier 2

    MVP Build

    $15,000 – $35,000
    6–10 weeks · deployable system

    A deployable system that one team can actually use day to day. Includes auth, basic monitoring, a feedback loop, and a runbook. Not yet hardened for company-wide rollout, but real enough to generate adoption signal you can take to the next budget cycle.

    • Production-grade code & tests
    • Deployed on your cloud
    • Eval, observability & runbook
    Scope an MVP
    Tier 3

    Production Engagement

    $50,000 – $150,000+
    3–6 months · full integration

    Full integration with your ERP, CRM, identity stack and data pipelines, hardened for company-wide rollout, with handover training, on-call SLA and a documented operating model. This is what an enterprise rollout actually costs when done honestly.

    • Full SSO & audit integration
    • Handover training for your team
    • 3 months SLA-backed support
    Plan a rollout

    info What is not included — the honest list

    Tier prices cover our engineering effort. They do not include third-party costs that pass through to you at cost: LLM API spend (OpenAI, Anthropic, Google), vector database hosting (Pinecone, Weaviate Cloud), GPU cloud time for fine-tuning (Modal, RunPod, Lambda), managed observability seats (LangSmith, Langfuse), or annotation labour for labelled datasets. We forecast these in writing during the pilot so there are no surprises, and we are happy to deploy on your own cloud accounts so you control the bill directly. We also do not pad estimates with discovery hours we did not run, integration work that is not actually scoped, or a "buffer" that quietly becomes margin.

    How We Engage — 5-Step Process

    Most AI projects fail at process, not at modelling. Our five-step engagement is deliberately the same on a $5K pilot and a $150K rollout — the depth scales, the discipline does not.

    STEP 1

    Discovery

    We sit with the people who do the work today, map the current process step by step, and pick the highest-ROI use case. Output: a one-page brief and a written go/no-go recommendation. Vague briefs are the single biggest reason AI projects fail.

    STEP 2

    Eval-First Design

    Before any model code we build a 30-300 case eval suite with expected outputs. Every prompt change, model swap and framework upgrade is then measured against this suite — converting "feels better" into a defensible number.

    STEP 3

    Build with Guardrails

    We pick the orchestration framework and model mix, build typed tool wrappers around your APIs, and add input/output guardrails, rate limits and per-session cost caps from day one — not bolted on at the end.

    STEP 4

    Pilot

    The system runs in shadow mode against real traffic, then in supervised mode, then autonomously on a slice of volume. We progressively widen the autonomy boundary as the eval scores hold — never with a flag day.

    STEP 5

    Handover & Training

    Two-week shadowing handover: your team owns the runbook, the eval suite, the dashboards and the deployment pipeline. No vendor lock-in, no black box. We stay on retainer for ad-hoc questions if you want it.

    Browse the Full AI/ML Service Catalog

    Beyond the six core practice areas, we ship NLP, computer vision and analytics work — search the full catalog below.

    search

    Frequently Asked Questions

    The questions buyers actually ask before signing — answered honestly.

    hjLabs.in prices AI/ML consulting by engagement scope, not by hour or token. A two-to-four week Pilot Sprint that proves a single use case typically lands between USD 3,000 and 7,000. An MVP Build that ships a deployable system to one team runs USD 15,000 to 35,000 over six to ten weeks. A Production Engagement with full integration, eval harness, observability and handover sits between USD 50,000 and 150,000+ and runs three to six months. Compared with US or UK consultancies our blended rate is roughly 40 to 60 percent lower while the senior engineering bar stays the same. We share a written scope and fixed-price quote before any contract is signed so finance teams can budget precisely.

    Project timelines depend almost entirely on how clean your data and integrations are, not on the model. A focused proof of concept against a single dataset is demoable in two to four weeks. A production-grade RAG system, agent or vision model with monitoring and CI/CD typically ships in eight to sixteen weeks. Full enterprise rollouts that integrate with ERP, CRM and identity systems run three to six months. We deliberately work in week-long sprints with a working build at the end of each sprint, so stakeholders can see real progress and steer scope early instead of waiting for a big-bang demo at the end.

    Yes. Roughly 80 percent of our engagement revenue comes from outside India — primarily the United States, United Kingdom, Canada, Australia, Germany, United Arab Emirates, Saudi Arabia and Singapore. We invoice in USD, EUR, GBP, AED or SGD and accept wire, Stripe, Wise and Payoneer. We work overlapping hours with US Eastern, US Pacific, UK and Gulf time zones, run weekly demo calls in English, and sign mutual NDAs and standard MSAs in your jurisdiction. Where required we sign Data Processing Agreements compliant with GDPR, HIPAA business-associate terms or UAE Federal Data Protection Law.

    Yes, and we encourage it. hjLabs.in routinely signs mutual non-disclosure agreements before discovery calls so you can share architecture diagrams, sample data and roadmap details freely. We accept your standard NDA template or send ours within 24 hours. For regulated industries we additionally sign Data Processing Agreements, Business Associate Agreements (HIPAA), and information security questionnaires. We can route sensitive data through your VPC, your cloud account or a self-hosted environment so no proprietary data ever leaves your boundary during the engagement.

    You do. Our default Master Services Agreement assigns full intellectual property of the deliverables — code, model weights fine-tuned on your data, prompts, eval suites, dashboards and documentation — to your company on final payment. hjLabs.in retains rights to general-purpose tooling we developed before the engagement and to anonymized lessons-learned. We will never reuse your data, your prompts or a model trained on your data for another client. If you require source escrow, we set that up with a neutral third party at the start of the engagement.

    Reliability is engineered, not hoped for. Every engagement starts by building an evaluation suite — typically 30 to 300 representative cases with expected outputs — that runs on every prompt change, model swap or data update. We layer guardrails using NeMo Guardrails or Guardrails AI, observability through LangSmith, Langfuse or Arize Phoenix, and human-in-the-loop checkpoints for high-stakes actions. We also enforce typed tool schemas and per-session cost caps to prevent runaway agents. Eval scores, latency and cost are tracked in a dashboard you keep at handover, so your team can prove the system is getting better over time instead of arguing from screenshots.

    Yes. We have shipped integrations with SAP, Oracle NetSuite, Microsoft Dynamics 365, Salesforce, HubSpot, Zoho, Tally, Freshdesk, Zendesk, Jira, ServiceNow, Slack, Microsoft Teams, MySQL, PostgreSQL and proprietary on-prem systems via REST, GraphQL, SOAP and direct database connectors. Where a system has no public API we build a thin adapter layer or use Playwright-based automation as a last resort. Authentication runs through OAuth2, SAML, API keys or service accounts, and every action our model takes is logged with the originating user identity so audit and compliance teams have a clean trail.

    Yes. Every Production Engagement ships with three months of bug-fix support included, plus an optional managed-service tier for monitoring, drift detection, model retraining, eval-suite expansion and prompt updates. Managed-service pricing typically runs 8 to 15 percent of build cost per year and includes a named on-call engineer with a four-hour response SLA in business hours. For clients who prefer to take ownership in-house we run a two-week shadowing handover with your engineering team, leave behind runbooks, dashboards and the full eval suite, and remain on retainer for ad-hoc questions.

    Ready to talk through your use case?

    Send the one-paragraph version of what you are trying to build. We will reply within one business day with whether it is a fit, an honest cost range, and the next step.

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    Explore our AI services: Agentic AI Development · RAG Systems · LLM Fine-Tuning · MLOps as a Service · Computer Vision · Predictive Maintenance · AI Chatbots · AI Summarization · Sentiment Analysis · Named Entity Recognition · Data Augmentation · Case Studies · Contact