Services
Four practices, one standard of finish.
We are hired when something matters enough that it has to actually work — a system the business runs on, an AI feature that has to hold up outside a demo, a product that has to reach a store. Each practice below lists what we deliver and the signals that say you need it.
Legacy Modernization
Move off the old stack without losing what it knew.
Most aging systems are not badly built. They are undocumented. The people who understood them left, the behavior lives only in the running code, and every proposed rewrite stalls on the same question: what does it actually do?
We answer that question first. We reverse-engineer a written, technology-agnostic specification from production behavior, get it reviewed by the people who run the business, and only then rebuild. The spec is a deliverable in its own right — it survives whatever stack we pick, and it is the artifact that makes a rewrite reviewable instead of a leap of faith.
What you get
- Behavioral specification reverse-engineered from production
- Domain model and business-rule inventory
- Rebuild on a current, supported framework
- Test coverage proving parity on the rules that matter
- Data migration with reconciliation
- Production deploy and handover documentation
You probably need this if
- The system runs on a framework that is out of support
- Two or more applications hold overlapping data
- Nobody on staff can explain a core workflow end to end
- It does not work on a phone and cannot be made to
In practice
AI Engineering
Systems grounded in your data, not demos that impress once.
The gap between an AI prototype and an AI product is not model quality. It is retrieval, latency, tool boundaries, evaluation, and knowing which parts of the problem should not involve a model at all.
We build retrieval pipelines over your own corpus, real-time voice agents that can be interrupted, multimodal systems that reason over live camera and audio, and tool-using agents with boundaries you can audit. The rule we hold to: if an answer is supposed to come from your material, the system is required to go and read it first.
What you get
- Corpus ingestion and normalization pipelines
- Retrieval design — embedding, indexing, and grounding policy
- Real-time voice and multimodal agent implementation
- Tool definitions with explicit scope and failure handling
- Latency budgeting across the full request path
- Evaluation harness for regressions in answer quality
You probably need this if
- A prototype demos well and fails on real questions
- Answers sound generic rather than like your organization
- Valuable knowledge is trapped in recordings or documents
- Voice matters and the current version feels like a phone tree
In practice
Product Engineering
Web and native from one core, taken all the way to the store.
We build the whole product: the data model, the interface, the offline behavior, the sync, the release pipeline, and the store listing. Shipping is part of the engagement rather than a phase handed to someone else at the end.
The architectural decisions that matter are made early and defended — what works without a network, what syncs, what lives on the device, and how much the first launch is allowed to cost the user in time and data.
What you get
- React and React Native applications from a shared core
- Offline-first data architecture and sync contract
- Performance work with measured before-and-after
- Authentication, accounts and cross-device state
- App store and Play Store release pipeline
- Versioned releases with a repeatable build process
You probably need this if
- You need web and mobile and cannot afford two teams
- The app is unusable without a strong connection
- First load is slow enough that people abandon it
- Valuable content exists but has no product around it
In practice
Cloud & Platform
Infrastructure sized to the organization that has to pay for it.
Cloud architecture goes wrong in two directions. Either it is a single virtual machine somebody logs into over SSH, or it is a platform team’s worth of infrastructure serving an application with four hundred users.
We size it to the actual load and the actual budget, and we close the credential problem properly — managed identity over stored secrets, wherever the platform supports it. Then we write down how it works, because infrastructure nobody understands is infrastructure nobody can change.
What you get
- Azure and Supabase architecture and provisioning
- Managed-identity authentication — no passwords in config
- Schema migration strategy and rollback path
- Background workers and scheduled jobs
- CI/CD pipeline from commit to production
- Cost review and right-sizing
You probably need this if
- Database credentials are pasted into configuration files
- Deploys are a manual sequence somebody remembers
- The monthly bill does not match the size of the workload
- There is no written description of what runs where
In practice
Next step
Tell us what is stuck. We will tell you what it takes.
The first conversation is a scoping call, not a pitch. You leave it with an honest read on feasibility, sequence and cost — whether or not we end up working together.