Engagement frameworks
How we structure the work.
Rather than list customer logos, this page documents the engagement patterns we run, the questions we answer in each, and the kinds of organizations they fit. Use it to decide whether what we do maps to what you need.
Engagement modes
Four shapes the work usually takes.
Architecture audit
Two to four weeks. A written assessment of the system: what is load-bearing, what is fragile, where the next year of growth breaks first, and what to fix in order. The deliverable is a document.
Focused proof-of-concept
Three to six weeks. One workflow, one integration, one model in production. The point is to retire a specific technical risk before the larger commitment, with the path to scale documented.
Implementation engagement
Multi-month milestones, time-boxed, with a written exit plan from day one. Code, infrastructure-as-code, CI pipelines, runbooks, and observability shipped against your repos and your accounts.
Standing advisory
Monthly cadence, exit on notice. For teams whose impact comes from a handful of high-stakes calls per quarter: architecture review, hiring loops, vendor selection, incident retrospectives.
Engagement archetype 01
Mid-market SaaS scaling past 1M requests per day.
The product works, the team is small, and the architecture from year one is showing fatigue. Tail latency is climbing, the database is the bottleneck nobody wants to touch, and the on-call rotation is starting to escalate every other week.
Questions answered
Where the system will break first under the next 4x of load. Which subsystems need horizontal partitioning vs vertical scaling. Where to invest engineering hours vs hardware budget.
Typical shape
Architecture audit, followed by a focused proof-of-concept on the highest-risk subsystem, followed by a phased implementation. Each phase has a written go/no-go decision point.
Deliverables
Architecture decision records, capacity model, observability baseline, runbook set, and an executable phased migration plan owned by your team.
Engagement archetype 02
Healthcare ISV preparing for a third-party audit.
An independent software vendor with healthcare customers has a contractual deadline to demonstrate alignment to the HIPAA Security Rule and to pass a SOC 2 Type II audit conducted by an independent CPA firm. The internal team has the product, not the audit-readiness paperwork or the control evidence pipeline.
Questions answered
Which administrative, physical, and technical safeguards are missing. Which Trust Services Criteria need control owners and continuous evidence. What the audit firm will likely ask for during walkthroughs.
Typical shape
Gap assessment audit, followed by an implementation engagement that adds policies, control evidence automation, and the access-review machinery the auditor will sample.
Deliverables
Risk register, policy set, control matrix mapped to HIPAA Security Rule and SOC 2 TSC, evidence-collection automation, and an audit-prep runbook the in-house team owns going forward.
Engagement archetype 03
Series B fintech rebuilding the event pipeline.
The first version of the event pipeline was a sequence of queues stitched together by the team that originally launched the product. Ledger correctness now requires exactly-once semantics, full audit trails, and a replay primitive the operations team can run without engineering intervention.
Questions answered
Where the current pipeline drops messages or duplicates them. Whether the new architecture should be log-based, store-and-forward, or hybrid. How to migrate without downtime or backfill drift.
Typical shape
Architecture audit on the current pipeline, focused proof-of-concept on the chosen pattern with realistic shadow traffic, then phased cutover with reconciliation against the legacy stream.
Deliverables
Event schema and contract docs, replay tooling, reconciliation harness, on-call playbooks, and a written rollback plan for each migration cutover.
Engagement archetype 04
Enterprise integrating AI into a customer-facing surface.
A large organization wants to add an LLM-powered feature to an existing product without compromising the safety, audit, and data-residency posture the rest of the platform already has. The hard part is the production posture: evals, prompt governance, retrieval safety, incident response, and the exit plan when a model is deprecated.
Questions answered
Which data the model may touch and where it goes. How to evaluate model behavior continuously, not only at launch. What happens when the chosen model is retired and a migration becomes mandatory.
Typical shape
Architecture audit on the proposed AI surface, focused proof-of-concept including evals and a model-failover path, followed by an advisory cadence during the first quarter of production.
Deliverables
Eval harness, prompt and retrieval-source registry, abuse-handling runbook, model-deprecation contingency plan, and a launch-readiness review document.
Your situation in one of these shapes?
Tell us the constraint, the deadline, and what you have already tried. We return a written outline of how we would scope the engagement within one business day.