AI engineering, taken to production
We take your AI from demo to production.
Agents and LLM features that work on real customer data, connect to the systems your business runs on, and can be trusted to act. Built by senior engineers in your repos and your cloud, with the evals, tracing, and guardrails that keep them working after we hand over.
Our engineers have built at
Property FinderMost AI pilots stall between the demo and the customer.
The demo only covers the happy path
It works on the five documents someone tried. Real inputs are scanned, multi-currency, and half-filled, and with no eval set nobody notices when a prompt change makes it worse.
Nobody can say what the model did
No traces, no cost line, no record of which tool call changed what. The first serious incident becomes an argument instead of a lookup.
The safety rules live in a prompt
Which customer's data the agent may see, and what it may change, is a polite instruction to the model. A prompt is a request, not a constraint, and a security review will ask the difference.
A finance agent that posts to real ledgers, for many companies at once.
An AI accounting platform needed its agent to read invoices and bank statements, reconcile them, and write the results into each client's own ERP, without one client ever seeing another's books. We built the production system around the model.
- Writes
- Only after a person confirms. The model proposes; deterministic code computes every amount and performs the write.
- Isolation
- Forced row-level security in Postgres. The agent's connection cannot read another client's rows.
- Integrations
- Odoo and Zoho Books, through one write path that is idempotent and reads back what it wrote.
- Inputs
- Invoices, bills, and bank statements from PDFs, scans, and spreadsheets, checked before a draft is shown.
The model is the small part. We build the rest of it too.
A production AI system is mostly backend, data, infrastructure, and product. One team builds all of it, so nothing falls into the gap between an AI vendor and a dev shop.
AI & agent engineering
Agents, tool use, retrieval, and document understanding, built as production systems with the evals and tracing to prove they still work next month.
- Agents and tool-use systems
- Document extraction and RAG pipelines
- Evals, guardrails, and prompt regression tests
- Human approval for anything that writes
- LLM observability, tracing, and cost control
- Model choice, routing, and fallbacks
The backend it runs on
Data models, queues, and integrations that hold real customer data safely and connect to the systems your customers already use.
- Multi-tenant data isolation
- ERP, CRM, and third-party integrations
- Durable jobs, queues, and idempotent writes
- API and service design
Cloud, infrastructure & LLMOps
Model gateways, deployment, and monitoring as code, in your cloud account, run by a small team without a platform department.
- AWS architecture and hardening
- Model gateway, rate limits, and spend tracking
- CI/CD with schema and contract checks
- Monitoring, alerting, and runbooks
The product around the model
The interface people actually use: chat, review queues, approvals, and dashboards that make the AI's work visible and correctable.
- Chat and copilot interfaces
- Review and approval workflows
- Internal tools and dashboards
- MVPs and v1 platforms
What makes it production, not a prototype.
Every AI build ships with the engineering that keeps it correct, safe, and affordable once customers depend on it. We decide these in week one, when they are cheap, not after the first incident.
Evals before launch
A test set of real inputs, run on every change, so a prompt edit cannot quietly make it worse.
Tracing and cost
Every model call and tool call recorded, with latency and spend you can break down by customer.
Tenant isolation
Customer data separated by the database, not by an instruction in the prompt.
Human approval on writes
The model proposes. A person confirms before anything reaches a system of record.
Retries without duplicates
Durable jobs and idempotent writes, so a timeout never posts something twice.
Uptime at real load
Architected for the traffic you plan to have, with no single point of failure.
Three steps, from first call to a system you own.
Scope
A 45-minute call, then a written scope: what gets built, the milestones, and what done means. For AI work, done includes the eval set it has to pass. Where the scope is fixed, so is the price.
Build
Weekly demos against those milestones, in your repos and your cloud from day one. You watch progress in commits, not in status decks.
Hand over
Documented, deployed, and explained to whoever runs it next. Keep us on for support and iteration, or take it and go.
Three ways to buy the same engineering.
Fixed-scope build
A defined deliverable with milestones and a price agreed up front, built and handed over. Most engagements start here, including production-readiness reviews of AI you already run.
Milestone-billed · fixed scope
Ongoing engineering partner
A monthly retainer for continuous delivery when the roadmap keeps moving and the system needs someone who knows it.
Monthly retainer · cancel monthly
Embedded engineer
When you would rather have a senior engineer inside your own team, in your Slack and standups, working as a founding-level hire.
Monthly retainer · defined hours
The four things clients say made the difference.
See what we build in-house →Senior engineers, all the way down
The people who scope your build are the people who write it. Nothing is staffed down to a junior bench after the sales call.
Architected for the load you plan to have
We make the scaling decisions in week one, when they are cheap, instead of rewriting the system the quarter it starts failing.
Your repos, your cloud, your IP
Everything is built in your accounts and handed over documented. Nothing is held back and nothing needs buying out later.
We build our own infrastructure
Observability, memory, agent runtimes: when a build needs one of these, we reach for something we wrote and understand end to end, not a SaaS bill you inherit.
A decade of shipping, before StarkSphere existed.
10+ years building for top tech companies across big tech, fintech, and high-growth startups.
Shipped cloud infrastructure, payment systems, and large-scale ML products, and led a 6-engineer team.
Led core search infrastructure serving 2M+ listings across 5 countries.
Built fintech lending infrastructure at scale.
AWS Certified Solutions Architect. Production experience across Java, Go, Python, and Node.js.