The model is not your problem.
The 200 PDFs nobody owns are.
Azure OpenAI consultant for industrial operations. RAG on SOPs, safety MSDS, quality manuals and incident corpora. Document intelligence for AP, quality and compliance. Production-grade with citation, confidence thresholds and human evaluation sets.
Who this is for
Azure OpenAI built around your role
Grounded generative AI for industrial operations — the use-case each leader owns, mapped to the decision it changes. Azure OpenAI on your own data, governed with Microsoft Purview, not a public chatbot bolted on.
IT Head
The problem
Every team wants a GenAI pilot, and each one spins up a different public model with your documents pasted into a prompt box — no identity, no tenant boundary, no audit trail.
What we build
Azure OpenAI deployed inside your own tenant, wired to Microsoft Entra ID and Microsoft Purview with sensitivity labels respected — so the model reads your data without your data leaving your walls.
Generative AI inside your tenant, governed by Microsoft Purview
Head of Data
The problem
The pilots that impressed in the demo fall apart in production because nobody grounded the model on your data — the answers are fluent and wrong, with no citation to check.
What we build
Retrieval over OneLake and Azure AI Search so every Azure OpenAI answer is grounded in your own documents and cites the source paragraph — hybrid search, not vector-only.
Grounded answers with citation to source
Operations Director
The problem
Your team loses hours hunting through SOPs, work instructions and specifications in SharePoint — the answer exists, but finding the right page mid-shift takes too long.
What we build
A document-intelligence pipeline and an Azure OpenAI agent surfaced in Copilot Studio, so an operator asks a question and gets a cited answer from the current document set.
Cited answers from your own document set
CFO / document-heavy function owner
The problem
Invoices, contracts and specifications are still keyed and checked by hand, and you have no line of sight on what a production GenAI workload would actually cost or risk.
What we build
Azure Document Intelligence pipelines with confidence thresholds and human review, scoped and costed before build — with per-query cost telemetry so the production bill is modelled, not discovered.
Manual document handling cut, cost modelled upfront
The Problem
Patterns we see in every engagement
Most Azure OpenAI pilots die because the buyer asked 'where is the value' before the technical team asked 'where is the data'. We fix the data first.
Vector search is not search.
Pure semantic search misses exact-match queries like part numbers, SKU codes and standards references. We use hybrid (vector + keyword + BM25) every time. Vendors who tell you vector search is enough have not tested it on industrial document corpora.
Answers without citation are unusable.
Operations teams will not act on an LLM answer they cannot verify. Every RAG response we ship includes citation to the source paragraph. No citation, no deploy.
The model is not the bottleneck — chunking is.
Bad chunking → bad retrieval → bad answer regardless of which model you choose. We A/B chunking strategies during prototype. Most pilots that 'don't work' are chunking failures, not model failures.
Cost compounds at scale and ChatGPT Enterprise is not the answer.
ChatGPT Enterprise does not read your industrial documents, does not write back to your ERP, and does not sit inside Power BI Copilot or Copilot Studio. For operations workflows, Azure OpenAI plus a real RAG pipeline is the right shape — not consumer chat.
What we build
What we build
Three production patterns. Each one shipped end-to-end with citation, confidence threshold and human evaluation set.
RAG on industrial documents
Replaces
The 200 SOPs / MSDS / work instructions sitting in SharePoint that nobody opens because finding the right page takes 12 minutes.
- Document ingestion via Azure Document Intelligence — text, tables, headings preserved
- Semantic chunking — pages chunked by meaning, not by token count; tables preserved
- Embedding via Azure OpenAI text-embedding-3-large
- Retrieval via hybrid search (vector + keyword) in Azure AI Search
- Generation via GPT-4o (or GPT-4 Turbo for cost) with citation to source PDF page
- Surface via Copilot Studio agent or custom Power App
Plant managers and operators get sub-30-second answers to SOP questions with cited source. Compliance teams accept the workflow.
Document intelligence — AP, quality, compliance
Replaces
The AP clerk who keys 200 supplier invoice line items into SAP every morning.
- Azure Document Intelligence custom models per document type
- Confidence scoring — anything below 90% routes to human review automatically
- Power Automate flow writes the extracted record to ERP after match-check
- Audit trail per extraction — every write traceable to source document and confidence score
AP processing time drops 60–70%. Quality COA extraction becomes automated. Compliance teams get audit trail by design.
Predictive text classification
Replaces
The customer service ticket inbox where every ticket gets manually triaged and 30% misroute.
- Few-shot or fine-tuned classifier for tickets, NCRs, work orders
- Confidence threshold — high-confidence auto-routes, low-confidence to human
- Validated against held-out set before deployment, refreshed quarterly
- Integrated into existing ticketing tool — no new interface for the team
Triage time drops. Misroute rate falls. Ticket SLA compliance rises without adding headcount.
Production RAG operations — eval, monitoring, refresh
Replaces
The pilot that worked in demo and degraded in production because nobody monitored it.
- Human evaluation set of 100–300 ground-truth Q&A pairs
- Monthly scoring against the eval set — drift surfaces immediately
- User feedback loop — every thumbs-down logged and reviewed
- Periodic re-embedding when source corpus updates significantly
RAG quality is measured, not asserted. Production agents stay accurate as the corpus evolves.
Cost engineering for production GPT workloads
Replaces
The pilot bill that was USD 200 and the production bill that became USD 18,000 — without anyone noticing.
- Per-query cost telemetry — track input tokens, output tokens, model used
- Model routing — GPT-4o for hard queries, GPT-4 Turbo for medium, GPT-3.5 for long tail
- Prompt caching where Azure OpenAI supports it
- Cost dashboard surfaces top-cost users and top-cost query patterns
Production AI cost stays modeled and controlled. Surprises do not land on the CFO's desk.
Tenant residency, identity, sensitivity
Replaces
The 'we can't put confidential data into ChatGPT' wall that blocks every regulated workload.
- Azure OpenAI deployed in your Azure tenant — prompts stay in your tenant
- Entra ID for agent access — same SSO as Power BI and Fabric
- Sensitivity labels respected — labelled content does not leak across teams
- Regional deployment (UAE, Saudi, India, Singapore) where sovereignty matters
Compliance teams sign off. Regulated workloads — pharma, finance, government-adjacent — become tractable.
Business outcomes
What changes once the model is grounded
Capability outcomes apply to every grounded build; the timeline shown is the MDI delivery cadence. Document-processing time savings are real but vary by corpus — we model yours before the prototype rather than quote a number here.
6 weeks
To first working grounded use-case
MDI standard cadence
Every Azure OpenAI answer grounded in your own documents and cited to the source paragraph
Retrieval over OneLake and Azure AI Search — hybrid search, not vector-only
Document intelligence for invoices, contracts and specs — confidence-scored, with human review below threshold
Tenant-resident and identity-aware — Microsoft Entra ID, sensitivity labels, Microsoft Purview
Per-query cost telemetry so the production GPT bill stays modelled, not discovered
A human evaluation set scoring answer quality month over month
Surfaced where teams already work — Copilot Studio, Microsoft Teams or a Power Platform app
How we work
From document audit to first production agent in 7–8 weeks
We start with the documents and the ground-truth Q&A set. Without those two, the build is theatre.
01
Discover — score the corpus, define the queries
Two weeks. Score the document corpus. Define the 3–5 highest-value queries the agent must answer correctly. Establish the human evaluation set — 100–300 ground-truth Q&A pairs.
02
Prototype — full RAG pipeline on 200–500 docs
Three to four weeks. Build the RAG pipeline end-to-end. A/B chunking, embedding and retrieval strategies. Evaluate against the human set. Target ≥85% acceptable answers before declaring readiness.
03
Deploy — full corpus, monitoring, train users
Four to eight weeks. Scale to full corpus. Surface in Copilot Studio or custom Power App. Train users on what the agent is good at and not. Establish monthly eval refresh.
Technology stack
AI Foundation
Retrieval
Document Ingest
Surfaces
Eval & Ops
Governance
Why MyData Insights
Why choose MDI for Azure OpenAI consulting
Microsoft-stack specialists
We build only on the Microsoft stack — Azure OpenAI, Azure AI Search, Microsoft Fabric, Power Platform. Your IT team supports what we deliver, with no niche-vendor lock-in.
The data is the project, not the model
Most pilots that fail are chunking and retrieval failures, not model failures. We ground Azure OpenAI on your own corpus over OneLake and Azure AI Search before we touch the prompt.
Governance-first
Azure OpenAI in your tenant, Microsoft Entra ID for access, sensitivity labels respected, Microsoft Purview for oversight — so regulated workloads clear compliance instead of stalling at it.
Every answer cited
No citation, no deploy. Each response carries the source paragraph so operations teams can verify before they act — fluent-but-wrong is not shippable.
Cost modelled before build
Per-query telemetry and model routing mean the production GPT bill is sized before the prototype, not discovered on the first invoice from Microsoft.
First working use-case in six weeks
A grounded Azure OpenAI use-case in production in six weeks on a fixed scope — not a 50-slide roadmap to review.
Common questions
What buyers ask us
Why Azure OpenAI vs OpenAI direct?
Tenant data residency, identity (Entra ID), sensitivity-label awareness, and regional deployment (UAE, Saudi, India, Singapore — where it matters for sovereignty). Same model. Different governance surface.
What about open-source models — Llama, Mistral?
Valid for some workloads, primarily where cost or fine-tuning control matters. We have shipped Mistral and Llama 3 on Azure ML for specific clients. Default for industrial-document RAG is GPT-4o because accuracy floor matters and user trust margin is thin.
Can we just give ChatGPT Enterprise to our team?
For generic productivity, yes — and many of our clients do. ChatGPT Enterprise does not read your industrial documents, does not write back to your ERP, and does not sit inside Power BI Copilot or Copilot Studio. For operations workflows, Azure OpenAI plus the build above is the right shape.
How do we know the answers are correct?
Three layers: citation to source for every answer; human evaluation set of 100–300 ground-truth Q&A pairs scored monthly; user feedback loop where every thumbs-down logs and we tune. We do not promise 100% — we promise the floor and we measure to it.
How much does it cost?
We work in fixed-scope, fixed-fee phases — Discover, Prototype, Deploy — never open-ended time and materials. The fee for each phase is quoted precisely after Discover, once we have seen your corpus size, query patterns and integration scope, because those are what actually move the number. What we commit to upfront is the timeline: first working output in 6 weeks, not a 50-slide roadmap. Azure consumption is separate and billed by Microsoft, not us — expect USD 800–18,000/month at production scale, which is exactly the range we model before the prototype rather than after the first bill.
Engagement
Free Azure OpenAI Use-Case Review
Thirty minutes with Amit on your actual document corpus and the questions your team keeps asking it — which use-cases are worth grounding first, and what a production Azure OpenAI build would cost and risk. No slides, no obligation.
What you get
- Two or three grounded GenAI use-cases scored by value and feasibility
- A data-grounding and governance readiness check across OneLake, Azure AI Search and Microsoft Purview
- A cost and risk read on a production Azure OpenAI workload
- A 6-week roadmap to your first grounded use-case
Ready to move
Book a 30-minute Azure OpenAI diagnostic
30 minutes with Amit. No slides. No pitch deck. No obligation to proceed. We walk through your document corpus and the queries the agent would answer first.