Most AI projects work in a demo, then stall before real users touch them. We're the senior team that gets mid-market companies from prototype to production — without enterprise price tags or a six-month discovery phase.
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💡 The problem is rarely the AI. It's how the project was scoped, built, and handed over. That's what we fix.
Four ways we help. Pick the one that matches where you are today — each links to the full detail below.
AI strategy consulting. We audit your data and workflows and hand you the 2–3 use cases with the fastest ROI — a build plan, not a strategy deck.
See how this works →Generative AI consulting services. Production-grade GenAI and LLM systems: retrieval that scales, context engineering, MCP integration, and sub-agent design that holds up outside a demo.
See how this works →Machine learning consulting. Forecasting, churn prediction, look-alike modeling, and anomaly detection — built on your data and wired into the tools your team already uses.
See how this works →AI integration consulting. Document processing, workflow automation, and copilots that connect to your ERP, CRM, and data warehouse — and get used.
See how this works →Most engagements stall because nobody knows what saying yes actually looks like. Here's exactly what happens when you work with us.
Already have a prototype? A senior engineer runs a hands-on architecture review and hands you a specific fix list. Starting fresh? We map your workflows to the 2–3 highest-ROI use cases — a build plan, not a strategy deck.
We develop a working solution against your real data, test it in your environment, and validate it with your team before a single integration happens.
We integrate into your systems and run a hands-on upskilling programme so your engineers own and extend the system after we leave — not just watch us build it.
The people who scope your AI project are the people who build it. Here’s who you work with, and the principles they hold themselves to.
Founded Perceptive Analytics in 2013, after roles at Infosys and Citibank. He has advised Fortune 500 companies and 350+ international clients, and his leadership earned the firm a place in Analytics India Magazine’s top 10 data analytics companies to watch. MBA (PGP) from the Indian School of Business. Teaches internationally on business analytics, data visualization, and dashboarding.
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Runs your engagement day to day — keeps scope tight, milestones clear, and your team in the loop from first call through production. The single point of contact who makes sure what we promised actually ships.
Connect on LinkedInWe measure success by client outcomes, not deliverables.
Our aim is always to deliver more value than asked.
Every consultant combines technical skill with industry context.
We invest in long-term partnerships built on trust.
Rapid iteration cycles that deliver value in weeks, not months.
Continuously pushing the boundaries of what data can do.
A few examples of systems real teams now depend on — not demos.
Specialized ai consulting firms expertise across verticals with unique regulatory, operational, and competitive dynamics
Commercial analytics, patient-journey models, payer coverage, and document automation for pharma and biotech — built for the data realities of a regulated industry.
Underwriting intelligence, claims automation, submission triage, and fraud detection for P&C and specialty carriers — AI that plugs into your core systems, not around them.
We also work across
You’ll talk to a senior AI consultant — not a salesperson. Bring your use case or your stalled pilot; you’ll leave with a clear next step.
Straight answers to what actually matters before a first call.
Yes — that’s some of our best work. We skip the discovery workshop and start with an architecture review: what’s production-ready, what needs re-architecting, and what will break at scale. You get a specific fix list, not a fresh strategy deck.
Most engagements go from first call to a working pilot against your real data in 3–6 weeks, and to production in under 8. No six-month roadmap before anything ships.
Senior engineers do the build; there are no juniors hidden behind a sales pitch. Every engagement ends with documentation, training, and a clean handoff, so your team can run and extend the system without us.
Every engagement is fixed-scope and priced up front, sized to a mid-market budget — no open-ended discovery tax before you know the number. The 30-minute working session is enough for us to estimate scope for your use case.
If you know AI should help but aren’t sure where to start, this is the entry point. We map your workflows and data, then hand you the 2–3 use cases with the fastest, most defensible ROI — a decision-ready plan, not a 40-slide deck you have to translate yourself.
Best for: teams early in their AI journey, or leaders prioritising a backlog before committing budget.
This is where most AI projects stall — and where we do our best work. We take a working GenAI or LLM prototype and make it production-grade, so it holds up under real users, real volume, and real edge cases. If you already have a prototype, we start with a senior engineer running an architecture review — not a discovery workshop.
Best for: teams with a promising prototype that isn’t reliable enough to ship.
Send us your architecture — we’ll review itWhen the answer is a model rather than a chatbot, we build the whole pipeline — not a notebook — and wire it into the tools your team already uses, so predictions show up where decisions actually happen.
Best for: teams with data and a clear prediction problem.
A model that can’t touch your real systems isn’t in production. We handle the last mile — the integration work most prototypes never had to do — and make sure your team adopts it.
Best for: teams automating a manual, high-volume process.
Deploying production-grade artificial intelligence requires far more than spinning up a cloud server and calling an API. Most enterprises fail at AI because they treat it as an IT experiment rather than a rigorous software engineering discipline. To guarantee your models scale securely and deliver measurable ROI, Perceptive Analytics utilizes a strict 6-phase implementation methodology.
We audit your existing prototype's architecture — data flow, model integration points, and where it's likely to break under real load — and score the gap between prototype and production against your specific tech stack.
Deliverable: Production-Readiness Gap Assessment
Who is involved: Lead AI Architect, Client IT Director/CTO.
We assess your data warehouses, vector databases, and SQL Server or ERP backends for the throughput and latency characteristics production traffic will actually demand.
Deliverable: Cloud Architecture Document
Technologies: Snowflake, Databricks, MS Fabric, AWS S3, Qdrant.
We build automated pipelines to unify data, tune vector databases for semantic search at scale, and establish the knowledge file taxonomy and context engineering structure — CLAUDE.md, compilation pipelines — your agents will run on.
Deliverable: Unified Data Pipelines & Context Engineering Framework
Technologies: Azure Data Factory, Pinecone, Milvus, Qdrant, dbt.
We construct a secure, isolated build — often multi-agent, using sub-agent design for heavy-payload queries — within your private cloud to validate architecture and logic without exposing production data.
Deliverable: Functional Multi-Agent Prototype/MVP
Technologies: LangChain, LlamaIndex, Anthropic Agent SDK, Python.
We engineer the "last mile": MCP integration architecture, idempotent transaction handling for ERP and CRM write-backs, and async task queues so AI-triggered actions behave safely under retries and concurrent load.
Deliverable: Live API, MCP Integration & Integrated Workflows
Technologies: Azure OpenAI, AWS Bedrock, GCP Vertex AI, MCP.
We establish MLOps infrastructure for automated retraining and performance alerts, and hand over a documented context engineering framework — CLAUDE.md, knowledge file taxonomy, compilation pipeline — so your internal team can maintain and extend it without us.
Deliverable: CI/CD Pipelines, Context Engineering Docs & Handover
Technologies: MLflow, Kubernetes, Azure ML.
We engineer predictive ML pipelines for Look-Alike Modeling and churn prediction. For risk management, we deploy RAG applications for instant policy querying, strictly governed by GLBA and SOC 2 compliance.
Predictive models for discharge rates and bed optimization. We bridge HIPAA compliance with advanced LLMs using zero-data-retention policies on AWS HealthLake or Azure API for FHIR.
Replacing legacy heuristic systems with machine-learning-driven optimization models. We ingest real-time weather and traffic data to continuously optimize transit routes near Hartsfield-Jackson hubs.
Applying anomaly detection algorithms to IoT sensor data for predictive maintenance. Our models identify micro-degradations weeks before failure, extending the lifecycle of heavy capital expenditures.
Gartner predicts over 40% of enterprise agentic AI projects will be canceled by 2027 due to unsustainable costs, unclear ROI, and inadequate risk controls. Most of that risk traces back to a handful of architecture decisions made — or skipped — before launch.
Use single-agent: For a well-scoped, single-domain task with a bounded
context window.
Use sub-agent orchestration: When queries span heavy payloads, multiple
data sources, or distinct tool-calling responsibilities — splitting the work reduces
latency and makes failures isolatable instead of catastrophic.
Any AI action that writes to an ERP, CRM, or database needs an idempotency key and a retry-safe design — otherwise a network timeout or an agent retry silently double-books an order, double-charges a customer, or duplicates a record.
Caching frequent queries, hybrid (keyword + semantic) search instead of pure vector search, and batching embedding calls are what keep a RAG pipeline sub-second at production scale instead of timing out under real concurrent load.
Comparing AI consulting firms? Look past the sales demo. For a mid-market company, the best AI consulting firm is the one that puts senior people on the work, ships to production instead of demos, sizes the scope to your budget, and hands your team a system they can run — here’s how to tell them apart.
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