An AI agent development company that gets the agent into production.
Most agent projects die between the demo and the deploy — at the point where the thing has to touch real data, real money, and real permissions. That gap is the work we take on: the tools an agent may call, the data it may read, the evidence it did what it claimed, and the tracing that tells you it still works next month.
Four pieces, and an agent needs all four.
A model behind a chat box is a weekend. The parts that take the time are the ones that decide whether it survives its first month with real users.
Agents wired into your real tools
An agent is only useful when it can act. That means your database, your ERP, your ticketing system — with the permissions and audit trail those imply.
- Tool-use agents that read and write in the systems you already run
- Model Context Protocol servers, so the same tools serve any client
- Human-in-the-loop steps where an action is expensive to undo
- Idempotency and retries, because an agent will call a tool twice
Retrieval over your own data
Most agent answers are only as good as what the agent was allowed to read, and most retrieval demos quietly ignore who is asking.
- Ingestion and chunking that survives real documents, not sample PDFs
- Retrieval scoped by tenant and by permission, enforced below the query
- Memory that outlives a session, so the agent stops starting from zero
- Evaluation on your own questions, not a public benchmark
Guardrails and evals
The question is never whether the agent works in the demo. It is whether you will know next month when it stops.
- Prompt regression tests that run in CI like any other test
- Refusals and fail-closed behaviour where the blast radius is real money
- Structured outputs validated before anything downstream trusts them
- A rollback path for a prompt or a model, not only for code
Observability and cost control
Most AI bugs are not logic bugs. They are “why did this call cost twelve cents and take nine seconds” bugs, and you cannot fix what you cannot see.
- Tracing on every model call, with latency and spend attached
- Per-tenant and per-feature cost, so pricing is a decision not a guess
- Alerting on the failure modes that do not throw
- Model and provider choice kept swappable, with a migration path off either
Three ways to buy the same engineering.
The work is identical; what changes is how it is scoped and billed. No open-ended retainers, and if none of the three fits, we say so on the first call.
Model 01 · Where most agent work starts
Fixed-scope agent build
One agent, one defined job, with milestones and a price agreed before anyone writes code.
We agree the scope and the milestones in writing, then bill against them as they land. You see a working demo every week in your own repository, so the first time you touch the agent is not the day it ships.
Commercials
Model 02
Ongoing engineering partner
A standing block of senior capacity once agents are live and the roadmap keeps moving.
Agents are not a project that finishes. Prompts drift, providers change their pricing, and the third integration behaves unlike the first two. This is the same engineers month to month, against a roadmap you set, so the context does not reset every time.
Commercials
Model 03
Embedded engineer
A senior engineer inside your team rather than alongside it — your Slack, your standups, your repo.
One named engineer, accountable to you, making the decisions a founding hire would make. Useful when you intend to own this capability yourself and want someone building it while your own team learns it.
Commercials
Agents we built, written up with the mistakes left in.
Every claim here has a write-up behind it. Read the decisions and judge the engineering rather than the adjectives.
Case study
An agent posting real invoices for many companies, out of one database
The platform already had row-level security on about seventy tables, and it was inert: the app connected as a role that bypasses it, and the policies read a claim nothing ever set. We moved tenant isolation into Postgres and made it impossible to merge a table without it.
Engineering note
One interface, three ERPs that agree on nothing
Odoo, Zoho Books and ERPNext disagree about what a posted invoice is, where an idempotency key can live, and whether a document can be un-posted. Adding the third one is where the adapter boundary either holds or you find you have three products.
Engineering note
Giving an agent memory that survives the session
Stuffing everything into the prompt works right up until the context window fills and the cost climbs. Memory you can write to, query and prune is a different thing, and it is the difference between a demo and a product people return to.
Senior engineers in India, building for founders in the US and Europe.
StarkSphere Labs is registered in Dubai and the engineering is done from India, for founders who are mostly in the United States and Europe. We overlap your morning, and the work carries on while you sleep.
That is also why this costs less than a US or UK agency of the same seniority: it is where the engineers live, not a different standard of engineer. We do not bid against $20-an-hour shops, and we do not charge Bay Area agency rates for work done from Bengaluru.
The engineers are senior — eight years and up, out of big-tech and fintech teams — and they are the people you meet on the first call, not a bench you are introduced to afterwards.
Working with us
What founders ask before the first call.
What does an AI agent development company actually do?
It builds the system around the model. The model is the part you do not write: the work is the tools an agent may call, the data it is allowed to read, the checks on what it produces, the audit trail of what it did, and the tracing that tells you it still works next month. We scope that, build it in your repositories, and hand it over.
How long before the first agent is in production?
A 45-minute call, then a written scope with milestones you approve before any code is written. Access and the first commit land in week one, and every week after that ends in a working demo against a milestone rather than a status deck. A first agent is usually a matter of weeks, not quarters — the honest variable is how much of your data and permissions work has already been done.
What does it cost?
Fixed scope, quoted in writing before anyone starts, billed against milestones as they land. Scope changes are re-quoted rather than absorbed quietly and invoiced later. The budget bands on our contact form run from under $10k to $50k+, and telling us which one you are in on the first call saves both of us a fortnight.
Which models and stacks do you work with?
Anthropic, OpenAI and Bedrock, in Python and TypeScript, with MCP where tools should be reusable across clients. We keep provider choice swappable on purpose, so a price change or a capability change is a configuration decision rather than a rewrite.
You are in India. Does that work for a US or European startup?
It is most of what we do. The company is registered in Dubai, the engineering is done from India, and we overlap your morning — so you get a working day of progress before your standup. Everything is built in your repositories and your cloud accounts, so the IP is yours from the first commit rather than at the end of the engagement.