Four fronts, one way of working.

Product Engineering

You have a product to stand up and no months to spend assembling a team — and no room to get the foundation wrong and find out at launch.

End-to-end engineering, from zero: a short discovery to cut scope, the first slice in production within weeks, and the product evolving in cycles from there. No big bang, no prototype that has to be thrown away later.

• 01Scope cut into slices, with the first delivery defined on paper
• 02Application in production, with CI/CD and separate environments
• 03Documented API and data model
• 04Observability and alerts from the first week
• 05Repository, cloud, and domains in your company's name

• How soon do I see the first version live?+

  Four to eight weeks for the first useful slice, depending on the scope cut during discovery. It isn't a prototype: it's code in production, with deploy and monitoring.
• Can you work alongside my in-house team?+

  Yes, and it's the format that works best. The in-house team joins the same repository and the same rituals; knowledge transfer happens during the project, not in a meeting at the end.
• Who owns the code in the end?+

  You do. Repository, cloud accounts, and domains stay in your company's name from day one.

Platform Engineering

Deploys that stall, incidents nobody knows who answers, and a cloud bill growing faster than the product.

Measure before touching anything. First the numbers — deploy time, failure rate, recovery time, cost per environment — then the changes that move those numbers, one at a time, starting with the ones that cut risk without touching the product.

• 01Automated deploy pipeline, identical across every environment
• 02Infrastructure as code, versioned
• 03Dashboards and alerts with a named owner and a written action
• 04Incident runbook and a rehearsed rollback
• 05Backup restore test running on a schedule
• 06Cost review per environment and per service

• Can this be done without stopping delivery?+

  It can. The order is always the same: first what cuts risk without touching the product — tested backups, alerts with an owner, a rehearsed rollback — then the infrastructure underneath.
• Do you keep operating the platform afterwards?+

  Only if you want us to. The default is to hand it over with a runbook and train the team. Ongoing support is a separate contract, never a dependency created on purpose.
• How soon does the first gain show up?+

  The first two weeks usually give back a working restore test and alerts with an owner — which is where the real risk lives.

• 45 minutes, $460 million: when technology governance is missingSep 20, 2026

AI Systems

The demo delights the board and falls apart in the first week of production: latency outside the agreement, a token bill with no explanation, and answers nobody can check.

Treating AI as a system, not as magic: a latency budget per step, retrieved context with the source cited, evaluation against real questions, and cost measured per feature — not by impression.

• 01Ingestion, chunking, and embedding with incremental reindexing
• 02Vector search with filtering and measured recall
• 03Evaluation set with real questions and a score per version
• 04Cost dashboard per feature, with cache verified
• 05Deterministic fallback path for when the model goes down

• Will my data train the model?+

  No. The documents stay in your database and the calls run under contracts that don't use your content for training. That is an architecture and a contract decision, and it is written into the project.
• What does it cost to run this per month?+

  It depends on volume, but it becomes predictable once it is measured. Indexing a corpus of a few hundred chunks costs cents; the real spend is in the generation calls — and that is where prompt cache and context size bring the bill down.
• Can it run on an open model, in my own cloud?+

  It can. The architecture separates retrieval from generation, so swapping the model becomes a cost and quality decision, not a rewrite.

• RAG in production: what changes once there's an SLASep 20, 2026

Legacy Modernization

A system nobody wants to touch, that one person truly understands, and that holds up everything else in the business.

No two-year rewrites. Map what exists, fence the current behavior in with tests, and migrate slice by slice — with the old system live until the last one, and a way back at every step.

• 01Map of dependencies, risks, and single points of failure
• 02Characterization tests over the current behavior
• 03Slice plan, with order and cut criteria
• 04Incremental migration, with a way back at every step
• 05Living documentation of what moved and what stayed

• Does the operation have to stop?+

  No. Each slice goes in with the old system still standing; the switch only happens once the new slice has proven it answers the same.
• What if there is no documentation at all?+

  That is the most common case. The characterization tests become the documentation: they record what the system does today, including what it does wrong and the business already depends on.
• How long does it take?+

  The audit and the slice plan take two to three weeks. The migration is measured in slices delivered, not in a single deadline — and you can stop between one and the next.