FIG. 01 — YOUR PRODUCT + AI
Add AI to your product — production-grade, in three weeks.
You’ve seen the demos that die in security review — the pilot that lived in staging for five months and quietly disappeared. This is the other kind: one AI capability, built inside your product, live in production in three weeks — with the numbers to prove it works.
Built for the product team told “AI by Q3” with no team to build it — and the innovation lead who can’t afford another pilot that never leaves staging.
FIG. 02 — WHAT QUALIFIES
One capability. Done properly.
We add exactly one production AI capability per engagement. That constraint is why it ships in three weeks — and why it works when it does.
- 01 — ASSISTANTS OVER YOUR DATA
Answers from your data, not the open internet.
An assistant grounded in your documents, tickets, and records — retrieval-based, permission-aware, with citations your team can check.
E.G. POLICY Q&A OVER 40,000 DOCUMENTS
- 02 — DOCUMENT INTELLIGENCE
Documents in. Structured decisions out.
Extraction and classification from invoices, claims, and contracts — with confidence thresholds and a human-review queue for everything below the bar.
E.G. INVOICE EXTRACTION · 500/DAY
- 03 — WORKFLOW COPILOTS
Your team’s slowest step, accelerated in-product.
Drafting, summarizing, and next-step suggestions inside the screens your team already uses. A human accepts, edits, or rejects — the copilot never acts alone.
E.G. CASE SUMMARIES DRAFTED IN YOUR CRM
- 04 — PREDICTION & CLASSIFICATION
The judgment call your ops team makes 200 times a day.
Scoring, routing, and anomaly flags with measured accuracy — and an escalation path for every case the model shouldn’t decide.
E.G. LEAD ROUTING · RISK FLAGS
What we won’t bolt on
No chatbot theater.
We won’t staple a chat window onto your product and call it a strategy. We don’t train models from scratch, we don’t build MLOps platforms, and we don’t do two capabilities at once. And if the honest answer to your problem is an off-the-shelf subscription, that’s the recommendation you’ll get in writing.
“If a $20/month tool solves it, we’ll tell you to buy the tool.”
FREE 2-HOUR TECHNICAL PRE-CHECK · BEFORE ANY CONTRACT
FIG. 03 — THE 3-WEEK SHAPE
Three weeks, measured in Fridays.
It starts before the contract: a free 2-hour technical pre-check on your codebase. Pass that, and the clock below starts on a Monday.
- WEEK 1▪ DEPLOYEvidence + Blueprint
On your codebase. Candidate models tested on your data, written recommendation, quality baseline agreed and signed. Friday: eval harness live in your CI.
- WEEK 2▪ DEPLOYBuild
Production integration inside your environment. Guardrails, monitoring, and cost controls in from the first commit. Friday: capability working behind a flag.
- WEEK 3▪ DEPLOYBuild + Launch
Hardening against the eval baseline, rollout to real users, handover docs, 60-minute team walkthrough. Friday: live in production.
3 FRIDAYS · 3 DEPLOYS · 0 SLIDE DECKS
$24,500 FIXED · 50% TO START · 50% END OF WEEK 2
The capability ships to production at the quality baseline agreed in Week 1 — or we keep working at no charge. We don’t guarantee model magic; we guarantee the agreed acceptance criteria.
FIG. 04 — EVAL BEFORE SHIP
You’ll have numbers, not vibes.
The meeting that kills most AI pilots is the one where someone asks “how do you know it works?” — and the answer is a demo. Your answer will be a report. Forward this section to your security lead today.
- QUALITY BASELINE
Week 1 tests 2–3 candidate models on your real data. You see accuracy and cost per call for each before we commit — and the baseline you sign is the bar every deploy is measured against. If no model should pass, the written recommendation says “don’t use AI here.”
- GUARDRAILS
Grounding, output validation, confidence thresholds, and human-review paths wherever a wrong answer is expensive. The model proposes; the guardrails decide what reaches a user.
- MONITORING
Errors, latency, and cost per call — live from Week 2, in your dashboards. The eval harness runs in your CI: any change that drops below baseline doesn’t deploy.
- COST CONTROLS
Spend alerts and per-feature cost tracking from day one. We pick the cheapest model that passes your quality bar — no surprise invoice from a model provider, ever.
- DATA RESIDENCY
- YOUR CLOUD ACCOUNTS · NOTHING COPIED OUT
- ACCESS
- YOUR REPO + IAM · REVOCABLE DAY 1
- MODEL CALLS
- ENTERPRISE API TIERS · NO-TRAINING GUARANTEE
- EVALS
- HARNESS IN YOUR CI · BASELINE SIGNED WK 1
- MONITORING
- ERRORS · LATENCY · COST PER CALL — LIVE WK 2
- OWNERSHIP
- CODE, PROMPTS, PIPELINES, DOCS: YOURS
Every claim above is contractual · see acceptance criteria — FIG. 05
FIG. 05 — WHAT YOU KEEP
Every deliverable is a document you can hold.
Capability edition
The 1-page signed end state: the workflow, the users, the launch-day definition, the success metric.
Shown, not asserted
2–3 candidates tested on your data — accuracy, cost per call, recommendation in writing. Including “don’t use AI here” when that’s the finding.
The numbers the guarantee is written against
The evaluation harness plus the measured quality baseline: candidate scores, threshold, pass rate, cost per call.
Yours to run
The capability live in your environment, architecture and guardrail docs in your repo, plus a 60-minute recorded team walkthrough.
FIG. 06 — STRAIGHT ANSWERS
The questions your security lead will ask anyway.
Which model will you use?
The cheapest one that passes your evaluation bar. Week 1 tests 2–3 candidates on your real data; you see accuracy and cost per call before we commit. The choice is documented in the Model Selection Memo — and your team can revisit it later using the same harness.
Is our data used to train models?
No. We use enterprise API tiers with contractual no-training guarantees, and your data never leaves your accounts. The data path is drawn in the architecture doc you keep.
What if AI is the wrong tool for our problem?
We tell you, in writing. The Week-1 recommendation includes “don’t use AI here” when the evidence says so — and if a $20/month tool solves it, we’ll tell you to buy the tool. You’d stop after Week 1 having paid for the evaluation, not the build.
Can you actually work in our codebase?
That’s what the free 2-hour technical pre-check is for — before any contract. If your codebase fails the pre-check, we say so and tell you what would need to change first. We don’t take engagements we can’t ship.
What happens after the three weeks?
Most teams continue on the Growth Package ($12,500/mo): monthly evidence review, one improvement bet per month, same Friday cadence — the capability gets better on real usage data. Multiple capabilities or compliance needs route to Enterprise; AI strategy beyond one feature routes to Fractional CTO.
- Building something new, not adding to it? The 4-Week MVP Sprint →
- More than one capability, or procurement involved? Enterprise →
- Want the capability to keep improving on real usage? Growth Package →
FIG. Ω — THE END STATE
Three weeks from now, your product does this.
Bring the workflow. We’ll sketch the capability, the eval bar, and the three-week plan in 30 minutes — and you’ll leave with a draft launch definition either way.