Start from the outcome, not the service menu.
Most teams come to us with a goal, not a shopping list. Pick the outcome closest to your situation and we will shape the team, services, and plan around it.
Use case 01
Build an MVP
- The problem
- You need to validate a product idea with real users before the runway runs out — without burning the budget on throwaway code.
- How we approach it
- A senior squad shapes the smallest revenue-ready slice, ships it to production weekly, and instruments it so you learn fast and keep what works.
Outcome
A launchable V1 in weeks, not quarters — built on foundations you can scale.
Where we start
Scope this outcomeUse case 02
Modernize legacy software
- The problem
- A critical platform is slowing the roadmap, expensive to change, and risky to touch — but a big-bang rewrite would bet the company.
- How we approach it
- We modernize in safe slices: characterize the system, carve seams, migrate behind feature flags, and keep the business running through every cutover.
Outcome
Lower operational risk every quarter, with a roadmap that finally moves again.
Where we start
Scope this outcomeUse case 03
Add AI to operations
- The problem
- Promising AI prototypes never reach production because accuracy, governance, and trust are unsolved.
- How we approach it
- We wire retrieval, evaluation, observability, and human review loops into your real data so AI becomes a measured, monitored product capability.
Outcome
AI features your users — and your auditors — can actually trust.
Where we start
Scope this outcomeUse case 04
Reduce cloud cost
- The problem
- Cloud and platform spend is climbing faster than revenue, and nobody is sure which architecture decisions are driving the bill.
- How we approach it
- We rework architecture, delivery pipelines, and FinOps controls so reliability and unit economics move together — not against each other.
Outcome
Predictable spend tied to value, with reliability that improves as cost falls.
Where we start
Scope this outcomeUse case 05
Extend engineering capacity
- The problem
- Your internal team is stretched beyond capacity and hiring senior engineers fast enough is not realistic.
- How we approach it
- We embed experienced engineers who lead discovery, ship production code, and raise the bar from week one — inside your cadence, not beside it.
Outcome
More throughput and a higher bar, without a multi-month hiring cycle.
Where we start
Scope this outcomeWhat changes when delivery gets serious.
Composite outcomes from across our portfolio — anonymized, but representative of the patterns we see repeatedly.
- 6-week release cycles with manual QA gates
- 45% of sprints slip due to unplanned rework
- P95 latency > 1.2s on core transaction path
- Cloud spend growing 40% YoY without proportional traffic
- Daily deployments with automated regression
- 94% on-time milestone delivery, zero rollbacks
- P95 latency < 280ms after architecture rework
- Cloud spend flat while handling 3× traffic growth
- GPT demo impressive in slides, unusable in production
- No evaluation framework — quality measured by anecdote
- Hallucination rate unknown, no guardrails
- Legal and compliance team blocking launch
- RAG pipeline serving 12k queries/day with < 2s latency
- Automated evaluation suite running on every commit
- Hallucination rate < 1.8%, human review loop for edge cases
- Compliance sign-off achieved with audit trail
Not sure which fits?
Tell us the outcome you need next.
Tell us where you are stuck. An engineer replies within one business day with the right first step — even if that step is not us.