Four practices that overlap on purpose. An AI project usually needs data work; a data platform usually needs an application around it; all of it eventually needs someone on your side of the table.
Production AI on your own data — assistants, agents, document processing, and forecasting — built to be measured and maintained rather than demoed once.
Use-case assessment with a go / no-go recommendation
Retrieval pipeline over your documents, with permissions honored
Agent workflows scoped to a single business process
Evaluation sets, regression checks, and cost ceilings
Deployment into your cloud, with logging and runbooks
The foundation everything else stands on: migrations that finish, warehouses people trust, and pipelines that do not need a babysitter on Monday morning.
Cloud migration and landing-zone setup on AWS or Azure
Data warehouse and lakehouse design, modeling, and loading
ETL / ELT pipelines with monitoring and alerting
Cost review and rightsizing of what is already running
CI/CD, infrastructure as code, and environment hygiene
Custom software when off-the-shelf does not fit, and modernization when what you have works but cannot move.
Web and mobile applications, front to back
Legacy modernization and phased re-platforming
API design and integration between systems you already own
Automation of manual, spreadsheet-bound processes
QA, accessibility review, and release support
Engineers who join your standup, use your board, and are accountable to your roadmap. We recruit for the role you actually have, not the one that is easiest to fill.
Contract, contract-to-hire, and direct placement
AI/ML, data, cloud, and full-stack engineering roles
Technical screening by our own engineers before you see anyone
Onboarding support so week one is not a write-off
Rates and terms are quoted per engagement. Ask and we will send a number, not a range with an asterisk.
A defined deliverable, a fixed fee, and a date. Change requests are priced before they start, not billed after.
The problem is well understood and you want a known cost.
A small pod — engineer, lead, and part-time architect — working on your roadmap month to month.
The work is continuous and priorities shift more than once a quarter.
Individual engineers placed into your existing team, managed by your leads under your process.
You have the plan and the management capacity, and need hands.
Describe the situation in a few sentences. We will point at the model that fits, even when the answer is that you do not need us yet.
IT consulting and AI engineering for teams that need the work done, not the deck.
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