Work
What an engagement looks like.
Every engagement is shaped to the problem, but most follow one of a few patterns. These blueprints show how each one runs and what you have at the end. Talk to us about the one closest to your situation.
Taking an AI pilot to production in one quarter
Fits when
A promising proof of concept has stalled. It works in a demo, but nobody trusts it with real customers or real money.
Audit the pilot and build an eval set from real cases, including the awkward ones.
Harden it: grounding, guardrails, permissions, monitoring and cost limits.
Staged rollout behind feature flags, with a review queue for low-confidence results.
Handover with runbooks, dashboards and a ranked improvement backlog.
You end with
- A production system with real users
- Eval scores tracked week by week
- Quality, latency and cost dashboards
- Runbooks your team can operate
Digitizing a paper-heavy onboarding flow
Fits when
Applications arrive as scans and email attachments, and an operations team re-keys them into a core system.
Sample real documents and measure extraction accuracy field by field before committing to targets.
Extraction, validation and a review queue, run in parallel with the manual process for comparison.
Integration with the core system, rolled out one document type at a time.
Accuracy monitored per field, and reviewer corrections fed back into the evals.
You end with
- Structured intake with an audit trail
- A review queue for exceptions only
- Accuracy reports per document type
- Fewer manual touches per application
Mapping and replacing a legacy core module
Fits when
A critical system is old, thinly documented and blocks every new feature that touches it.
AI-assisted code mapping and a business rules catalogue, confirmed by your experts.
Characterization tests from production behaviour, and the first capability moved behind a facade.
Capability-by-capability migration with automated reconciliation.
Old components retired once the new ones have matched them in production.
You end with
- Documented business rules
- Characterization test suites
- New services in your cloud
- A retirement plan for what remains
Making a SaaS platform enterprise-ready
Fits when
Larger customers want SSO, audit logs, data isolation and uptime commitments the platform was not built for.
Architecture review and load tests against the enterprise requirements.
Tenant isolation, SSO and role-based access, and audit logging.
Usage metering, SLOs with alerting, and disaster recovery drills.
Platform team support while your engineers take ownership.
You end with
- Enterprise features customers ask for
- Documented SLOs and alerting
- Tested recovery procedures
- Architecture decision records
Adding an AI copilot to a B2B product
Fits when
Customers are asking for AI features, competitors are shipping them, and the team needs to move without breaking trust.
Find the tasks users repeat most and define what a correct answer looks like for each.
Retrieval over customer data that respects each user's permissions, and a first eval suite.
Beta with design-partner customers, with feedback captured as new eval cases.
General release with cost controls, usage analytics and quality monitoring.
You end with
- A copilot inside your product
- Evals that run on every change
- Cost per conversation tracked
- A roadmap built from real usage
Extending a team for a roadmap push
Fits when
The roadmap is set, hiring is slow and a launch date is fixed.
Brief, then a shortlist of vetted engineers matched to your stack and time zone.
Your interviews, then contracts and access.
Structured onboarding into your repositories, rituals and tooling.
Engagement lead check-ins, with scale up or down on notice.
You end with
- Roadmap capacity when you need it
- Engineers working inside your process
- Documented, reviewed code
- An option to hire engineers permanently
See your situation here?
Tell us which blueprint is closest and what is different about yours. We will come back with a plan.