You already use Claude. We put it into production
We design and build enterprise-grade AI on Claude: MCP servers, production agents, retrieval over your own data, and the cost controls that keep it affordable. Built inside your repos and your cloud, by people who were shipping production AI long before Claude existed.
Member of Anthropic's partner program. Every engagement is delivered by senior engineers, never juniors.
MCP
Code
Cowork
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Claude
What does a Claude consultant actually do?
Four things. We connect Claude to the systems it cannot see, build the parts that have to run unattended, prove it works before it touches a customer, and keep the bill under control. Everything ships inside your repos and your cloud, so you own every line.
Connect it to your data
MCP servers, API integration and retrieval over your own databases, so answers come from your records instead of a paste.
Make it run unattended
Production agents and Cowork routines with retries, fallbacks and alerting, so the work happens without anyone starting it.
Prove it before launch
Evals, guardrails and prompt-injection hardening. You find out from a test suite, not from a customer.
Keep the bill sane
Caching, batching and model routing applied to the workloads actually costing money, with the savings modelled first.
From a browser tab to a system that runs.
Claude in a tab
- Two or three people use it well, everyone else pastes prompts
- Your systems are invisible to it, so every task ends in copy and paste
- Answers sound right and cite nothing
- Work starts when somebody remembers to start it
- Nobody owns the bill and nobody can explain it
Claude in production
- Wired to your data through an MCP server you control
- Answers grounded in your real records, with citations
- Agents that run on a schedule with retries, fallbacks and alerting
- Evaluated before launch, monitored after
- Caching, batching and routing applied, so the bill stays sane
The systems we design and build.
These are the shapes of work we take on. Each one is a real engineering project, not a prompt pack, and each has a failure mode that only shows up in production. That is the part we are hired for.
Multimodal biometric verification
Five separate modalities, each its own research and engineering track: palm vein, iris and eye socket geometry, fingerprint, face, and speech. Different sensors, different model architectures, different accuracy and latency tradeoffs, fused into one identity decision with a confidence score you can defend.
Face recognition at national scale
A recognition pipeline built from first principles rather than wrapped around an SDK: detection, alignment, embedding and one-to-many matching, tuned to hold accuracy at airport throughput. The hard part is not the model, it is holding precision steady as the gallery grows into the millions.
Geospatial and satellite intelligence
Ingestion pipelines for satellite imagery, feature extraction from multi-spectral bands, and analytical models that turn raw scenes into something a non-specialist can act on. Cloud masking, revisit gaps and calibration drift are where these projects usually fail.
Anomaly detection at exchange scale
High-throughput transaction monitoring where the volume is enormous and the false-positive budget is tiny. Statistical detection finds the outliers, then Claude reads each flagged event in context and explains what it appears to be, so the analyst queue becomes a ranked shortlist instead of a wall.
Clinical and compliance documentation
A conversation or case file in, the structured record a regulator expects out. Speaker separation, transcription with domain vocabulary rather than generic speech models, then mapping to standardised fields. A human approves before anything commits, and every field links back to its source line.
Real-time video analytics
Object detection, activity recognition and event triggers running on live camera feeds, at frame rates that make the output useful rather than forensic. Built to survive bad lighting, occlusion and cameras nobody has cleaned in a year.
Ask your database in plain English
A retrieval layer resolves a plain-language question against your actual schema, Claude writes the SQL, and the answer returns with the query it ran. Grounded in the real table structure, so it cannot invent a column or a join. Reporting answered in seconds instead of a BI queue.
Document and imaging pipelines
Handwriting, scans, forms and photographs turned into clean structured fields your systems accept. The unglamorous work that quietly consumes a back office, and the place where accuracy on the worst ten percent of inputs decides whether anyone trusts the system.
Enterprise data harmonisation
Disparate sources reconciled into one analysable layer: schema mapping, deduplication, identity resolution and pipelines that keep working when an upstream system changes shape without telling you.
An MCP server for your product
Your product becomes a set of tools Claude can call directly, with permissions approved one tool at a time. Customers stop exporting data to paste into a chat window, and Claude Code and Cowork use your software like any other tool. We have shipped one to production, in the case study below.
Contract and policy intelligence
Long-context review that holds a whole agreement at once instead of sliding a window over it. Flags the clauses that differ from your standard position, explains how, and quotes the paragraph. Useful exactly where a summary is not.
Three ways in.
Build it
We design and ship the system, end to end, inside your repos and your cloud. Production standard, monitored, documented.
- MCP servers and API integration
- Production agents with fallbacks
- Retrieval, text to SQL, document pipelines
- Evals, guardrails and monitoring
Run it
The recurring work runs on a schedule instead of waiting on whoever remembers. Set up in Cowork, with guardrails and a handover.
- Claude Cowork setup and routines
- Claude Skills built from your SOPs
- Scheduled reporting and reviews
- Alerting when something drifts
Own it
Your team learns to build the next one. We would rather be the team you call for hard problems than the team you cannot function without.
- Claude Code rollout and conventions
- Hands-on team training
- Fractional AI CTO retainer
- Architecture and build reviews
We train your team in the room.
Most Claude rollouts fail quietly. The tool gets bought, a few people love it, everyone else goes back to the old way, and the licence renews anyway. Training is what stops that.
We run hands-on sessions with your actual work in front of us, not slides. Engineers learn Claude Code against your repo and your conventions. Operators learn the Skills and routines built for their process. People leave having done the thing, not having watched it.
- Sessions run on your codebase and your workflows
- House conventions and guardrails written down
- Recorded, so new joiners get the same start
That is our own Cowork workspace on the right: eleven routines on a schedule, a run history that has completed every morning, and an agent editing files in the repo. We teach your team to build exactly this, on your work.
Built end to end with Claude: Mero AI.
We have shipped a number of AI products. This is the one built entirely with Claude Code and Cowork, and the one running a production MCP server in the cloud, so Claude uses it directly as a tool rather than through a browser.
Mero AI
An AI product-decision partner, designed and shipped end to end: a full web app, native integrations, a documented REST API, developer docs, and an MCP server deployed to the cloud. The screenshot is Mero registered as a custom connector inside Claude, with its tools and per-tool permissions live. Built with Claude Code and Cowork throughout, from empty repo to production.
Mero is our own product, so treat this as a builder's note rather than a client reference. I built it the way I would build yours: Claude Code from an empty repo, Cowork running the recurring work, a public REST API, and an MCP server in the cloud so Claude uses it as a tool instead of a website. Everything on this page is something I have actually shipped.
Ishan VatsFounder, IV Consulting
Start with a consult. Own everything after.
Consult
We map where Claude belongs, design the architecture and tell you what to build first. You keep the plan either way.
One call to startBuild
Scoped separately. We build inside your repos and your accounts, ship to production with monitoring, then hand you the keys.
Fixed scope, fixed quoteHandover
Docs, conventions and training, so your team can extend it without booking us again.
You own it outrightWhat does Claude consulting cost?
Two shapes. A scoped build, priced as a fixed project once we know what we are building. Or a retainer, when you want us owning the Claude architecture and training your team over months. We do not list prices because we have never quoted two of these the same. Scope drives the number, and scope takes one call to establish.
Scoped build
Priced as a fixed project once we know what we are building. One outcome, one number, agreed before anyone starts.
Best when you know the thing you want shippedRetainer
We own the Claude architecture, review the builds and train your team over months. Senior judgment on tap without carrying the hire.
Best when you are shipping continuouslySenior engineers only. No junior bench.
Ishan Vats
Claude Partner Network member. Builders and engineers who have delivered for 150+ teams, backed by three decades of combined engineering experience across enterprise AI.
- Shipped with ClaudeAn AI product with a live MCP server, built end to end with Claude Code and Cowork, running in production today.
- Owns the engagementScoping, architecture and handover. The person on your first call is the person who owns your build.
Results
Every build is tied to one number that matters to you: revenue up, hours saved, or churn down. If it does not move that number, we do not ship it.
Reliability
What we build runs in production without babysitting, tested against your real workflow and fully documented. It stays stable long after we have handed it over.
Relationships
Most clients come back for the next build, because the first one worked and the people behind it were easy to reach. We stay reachable after handover, not just during the project.
Most Claude bills have levers on them nobody has pulled.
Teams ship the thing that works, traffic grows, and the invoice quietly becomes a problem nobody owns. We read where the tokens actually go, by project and by task, then fix the pattern causing it. These are Anthropic's published discounts, not our estimates, and most workloads qualify for more than one.
This is our own Claude usage, not a client's. We instrument our spend the same way we would instrument yours: cost and tokens by day, by tool and by model, so the expensive pattern is visible instead of theoretical. The number to look at is the cache hit rate. At 99.5 percent, with 2.08 billion tokens served from cache, almost none of that repeated context is being paid for at full price. That is the single lever most teams have never switched on.
A spend audit is a fixed-scope piece of work. We read your usage, model the savings, and give you the changes ranked by what they return. If the audit does not identify at least its own fee in annualised savings, we refund it in full.
The Claude playbook.
Everything we have written on putting Claude to work. Start here if you would rather read than book.
Everything else teams ask.
What is a Claude consultant?
What is an MCP server and do we need one?
How is Claude consulting different from your automation work?
Do we own everything you build?
Why are there no prices on this page?
Can you train our team instead of building it for us?
What can Claude actually see once the MCP server is live?
Tell us what you want Claude doing by next quarter.
Book a free 30-minute call. We will map what we would build first, what it would take, and whether you should build it yourselves.
You talk
Where Claude already helps, where it stalls, and what you wish it ran on its own.
We map
The right surface, the first thing to build, and the honest order to do it in.
You decide
You leave with the plan and a budget range. Building with us is a separate decision.