AI systems that survive contact with production.
We design, build, and ship the systems behind AI products — the pipelines, the platforms, the agents, and the guardrails that make them work in the real world.
The model is rarely the problem.
AI initiatives don't usually fail at the model. They fail at the seams — data that was never modeled, pipelines nobody can observe, agents with no path to deployment, architectures that were designed for a demo and then asked to carry a business.
We're engineers first. We build the parts that don't make it into the pitch deck: the ingestion layer, the evaluation harness, the orchestration, the observability, the failure modes. That's the difference between a proof of concept and a system your teams can depend on.
Multi-agent orchestration, retrieval pipelines, document intelligence, and natural-language interfaces to enterprise data. We architect for accuracy, cost, and traceability from day one — not as a retrofit.
Ingestion, modeling, and semantic layers that make AI possible in the first place. Lakehouse architecture, schema design, and data contracts built so the next system you add doesn't require starting over.
MLOps, AIOps, DevOps, and cloud infrastructure as code. Deployment pipelines, environment parity, monitoring, and cost control — so what we build stays shipped.
Boutique isn't a size. It's an operating model.
We don't sell a pyramid. The people who scope your engagement are the people who build it.
Senior by default
Principal- and staff-level engineers do the work, not just the sales call.
Differentiated roles
AI engineering, data engineering, and platform work are distinct disciplines. We staff them that way — generalists underserve real scope.
Direct access
No account layer between you and the team building your system.
Honest scoping
If a six-week engagement is the right answer, we'll tell you — and if the problem doesn't need AI, we'll tell you that too.
Built for environments where "close enough" isn't.
Our background is in domains where AI output has consequences — clinical operations, regulated healthcare data, and mission-critical logistics. Systems we've architected include compliance-aware workflow orchestration, clinical document intelligence at scale, multi-agent decision platforms, and event-driven service architectures on AWS.
That pedigree shows up in ordinary ways: we assume audit trails, we assume PHI handling, we assume someone will eventually ask how a given answer was produced.
Tell us what's stuck.
A short conversation is usually enough to tell whether we're the right fit. No pitch deck required.
Four disciplines. One delivery team.
Most AI problems are really data problems wearing a disguise — or platform problems, or product problems. We staff across all four so the diagnosis isn't limited by what we happen to sell.
AI Engineering
We build AI systems, not AI demos. That means architecture that accounts for latency, cost, evaluation, and failure — and code that a client engineering team can own after we leave.
- Multi-agent orchestration and agentic workflows
- Retrieval-augmented generation, including hybrid and graph-backed retrieval
- Document intelligence — extraction, classification, and structuring
- Vision-language extraction for documents that defeat conventional OCR
- Natural-language interfaces to structured data, backed by semantic layers
- Evaluation harnesses, guardrails, and human-in-the-loop review
Data Engineering
AI is only as good as the data layer underneath it. We design that layer to be queryable, governed, and reusable — so the second and third use case cost a fraction of the first.
- Batch and streaming ingestion pipelines
- Lakehouse architecture on object storage, open table and file formats
- Dimensional and semantic modeling, legible to analysts and language models alike
- Data contracts, lineage, and quality instrumentation
- Migration and consolidation of fragmented source systems
Platform & Cloud Operations
The unglamorous half of shipping. We build the deployment, monitoring, and cost discipline that keeps AI systems running after launch week.
- Infrastructure as code, reproducible environments
- CI/CD for models, prompts, and services — versioned and rollback-capable
- Containerized microservice and event-driven architectures
- Observability: tracing, drift detection, quality monitoring, spend attribution
- Local-first dev environments that mirror cloud behavior
AI Product & Delivery Leadership
Engineering capacity without direction burns budget quietly. Our product managers translate ambiguous business goals into scoped, sequenced, testable delivery — and say no to the work that won't pay for itself.
- Opportunity assessment and AI feasibility review
- Roadmap and sequencing, with explicit dependencies and decision points
- Requirements definition engineers can build from directly
- Fractional AI leadership for teams building in-house capability
- Internal enablement — standards, components, review practices
Industries
Healthcare & Life Sciences
Clinical documentation, diagnostics operations, revenue cycle, and regulated data handling.
Healthcare Logistics
Scheduling, routing, and multi-vendor integration for transportation networks.
Enterprise Operations
Supply chain, quality management, and document-heavy back-office processes.
Not sure which of these you need?
That's a normal place to start. Describe the problem and we'll tell you what it actually is.
Small teams. Short cycles. Nothing thrown over a wall.
Our engagements are built to produce working software early and decisions even earlier. You should know within weeks — not quarters — whether an idea deserves further investment.
Discovery Sprint
A fixed-scope assessment. We interrogate the problem, audit the data and systems, and deliver a target architecture, a build sequence, and an honest feasibility read. You leave with a plan you can execute with or without us.
Proof of Concept
A working system against real data, scoped to prove or disprove the riskiest assumption. Built on production patterns from the start, so a successful POC becomes the foundation rather than a throwaway.
Build & Embed
A dedicated pod — AI engineering, data engineering, platform, and product — building alongside your team. We work in your repos, your standards, your review process. Knowledge transfer is a deliverable, not a farewell gesture.
Fractional AI Leadership
Senior technical direction without a full-time hire. Architecture review, hiring support, standards, and course correction for teams building their own capability.
Delivery principles
Scope the blockers before the build
Domain expertise and usable data are prerequisites, not workstreams. We identify who owns each one before week one.
Working software over status decks
You see running code on a real dataset early, and every week after.
Design for the system you'll have in two years
We make the architectural choices now that keep the next phase cheap — even when the current phase doesn't require them.
Compliance is a design input
Auditability, access control, and data handling are architected in, not bolted on during a pre-launch scramble.
Evaluate continuously
Every AI component ships with a way to measure whether it's still working. "It seemed fine in testing" is not an operating model.
Leave it ownable
Documentation, runbooks, and architectural decision records are part of delivery. Our success condition is that you don't need us.
What makes an engagement work
The engagements that go well share three things. We'd rather say this up front than discover it in week three.
A domain owner
Someone who knows how the business actually operates and can answer questions in hours, not weeks.
A data owner
Someone who can get us access to representative data — real enough to be useful, governed enough to be legal.
A decision-maker
One person who can resolve trade-offs without convening a committee.
Technology
We're deliberately not religious about tooling — the right stack is the one your team can maintain. That said, this is where we're fastest:
AWS — including managed agent, event, and analytics services — with infrastructure defined as code.
Python, FastAPI, containerized microservices, event-driven architecture.
Open table and columnar formats, object-store lakehouses, serverless query engines.
Frontier and open-weight models, agent orchestration frameworks, hybrid retrieval, structured evaluation tooling.
Start with a two-week answer.
A Discovery Sprint is the lowest-risk way to find out whether this is worth building — and what it should cost.
A firm built the way we'd want to be hired.
InArt Consulting was founded on a straightforward observation: the gap between an AI prototype and a production system is where most of the value — and most of the failure — lives. We built a team to work in that gap.
We came out of enterprise AI, not out of a consultancy. Our founding experience is in regulated healthcare — building document intelligence at clinical scale, agentic systems under audit requirements, and the data platforms holding them up. That's a domain where an unexplainable answer isn't just poor UX; it's a compliance event.
That shaped how we build. We assume systems will be inspected. We assume requirements will change and someone will need to understand a decision made eighteen months earlier. We assume the client's team will eventually own the code, so we write it accordingly.
We stayed small on purpose. A boutique firm can staff senior people on every engagement, turn down work that isn't a fit, and put the person who designed the architecture in the room when it's questioned. That's hard to do at scale, and it's most of why clients come to us.
Our bench spans data engineers, AI engineers, and product managers at every level — from principals who've architected enterprise platforms to strong mid-level engineers who ship quickly under senior direction. We assemble the team around the problem, and we're explicit about who's doing what before you sign anything.
Deepak Sarin
FOUNDERAI and automation practitioner who turns complex knowledge work into reliable systems. He builds custom agents and workflows for research, document intelligence, compliance checks, and structured data extraction—especially in regulated domains where accuracy and auditability matter. At InArt, he focuses on embedding AI into real client processes so teams gain capacity without losing control of quality, compliance, or ownership of the stack.
Piyush Dhuliya
PARTNERStrategic product and customer success leader with 15+ years building and scaling AI/ML SaaS across government and enterprise. He has led the full customer lifecycle—from solution consulting and complex pursuits through implementation and long-term growth—partnering at the executive level to shape product vision, win trust with senior stakeholders, and turn AI platforms into durable business outcomes.
Say the hard thing early. A scoping conversation is cheaper than a failed build.
Build for the handover. Code you can't maintain is a liability we sold you.
Right-size the solution. Not every problem needs AI, and not every AI problem needs a frontier model.
Stay senior. We'd rather turn down work than staff it thin.
Let's talk about what you're building.
Send us a short description of the problem — current state, what you've tried, and what "working" would look like. We'll come back within two business days with an honest read on whether we can help.