What we build, how we build it, and what it leaves behind when we go.
Six service tracks across software and AI analytics. Each one ships with a senior-led crew of two to four people, tied to one outcome you can defend in a board meeting without a slide deck.
Product engineering, end to end.
Greenfield web apps, mobile clients, the APIs behind them. We scope it, architect it, ship it, and hand it to your team with the CI/CD pipeline, the on-call runbook, and the test suite already wired up. The boring parts done first.
- SVC · 01.AGreenfield product builds6–10 WK
- SVC · 01.BLegacy rewrite / strangler-fig migration8–14 WK
- SVC · 01.CAPI design + multi-tenant platforms4–8 WK
- SVC · 01.DMobile clients (React Native, native)6–12 WK
- SVC · 01.EAudit + architecture review1–2 WK
Internal platforms your team won't quietly resent.
The admin consoles, ops dashboards, and back-of-house tools your people live in eight hours a day. We build them with the same rigor as the customer-facing app. That's where margin actually leaks, and where morale goes to die.
- SVC · 02.AAdmin + back-office consoles4–8 WK
- SVC · 02.BWorkflow + approval systems4–8 WK
- SVC · 02.CInternal data exploration tools3–6 WK
- SVC · 02.DCLI + developer tooling2–6 WK
Analytics that answer questions, not raise them.
Real analytics work, not dashboards stacked on dashboards. We define the question with the person who has to make the decision, model the data honestly, and ship a surface that actually gets opened on Monday morning.
- SVC · 03.AOperational dashboards + KPIs3–6 WK
- SVC · 03.BCohort + retention analytics3–5 WK
- SVC · 03.CPricing + unit-economics modeling4–8 WK
- SVC · 03.DAnomaly detection + alerting4–10 WK
- SVC · 03.EExecutive reporting + narrative2–4 WK
OPS / Anomaly Detection · last 24h
● LIVEML pipelines and LLM systems that survive a quarter on call.
Forecasting, classification, ranking, retrieval, agentic workflows. We bring our own eval harness, our own drift monitors, and a strong preference for the smallest model that beats the benchmark. Big models are easy to brag about. They're hard to operate.
- SVC · 04.AForecasting (demand, churn, capacity)6–10 WK
- SVC · 04.BAnomaly detection / outlier monitoring4–8 WK
- SVC · 04.CRAG assistants on internal corpora6–10 WK
- SVC · 04.DDocument / PDF extraction at scale4–8 WK
- SVC · 04.EEval harness + model monitoring3–5 WK
Data platforms that don't fight you back.
Warehouses, lakes, streaming layers. Pipelines you can reason about, schemas you can search, lineage you can prove. We bring opinions on tooling, and we let them go when your stack tells us otherwise. Religion costs you money in production.
- SVC · 05.AWarehouse setup (Snowflake / BigQuery)4–6 WK
- SVC · 05.Bdbt model layer + lineage3–6 WK
- SVC · 05.CStreaming pipelines (Kafka / Kinesis)4–8 WK
- SVC · 05.DFeature stores for ML3–5 WK
Integrations with retries, replays, and a paper trail.
The connective tissue between your stack and everyone else's. Stripe, Salesforce, HubSpot, NetSuite, custom EDI partners, legacy SOAP services. Built with idempotency, retries, and a replay button on every failure, because networks blink and humans miss the first alert.
- SVC · 06.AThird-party SaaS sync layers2–6 WK
- SVC · 06.BEvent bus + webhooks + outbox3–6 WK
- SVC · 06.CEDI + legacy partner connections4–8 WK
- SVC · 06.DIdentity + SSO (SAML / OIDC / SCIM)2–4 WK
OPS / Integration health
● 4 SYNCSHave something specific in mind? Tell us the line item you need moved.
Two-week paid discovery is $9,200, credited toward the engagement if you proceed. You leave with a scope document, a risk register, and a fixed-price proposal. Whether or not you hire us.