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Service

Strategy written by people who ship.

Most AI strategy decks fail on first contact with engineering, because the authors never built anything. Our roadmaps come from engineers — every recommendation carries an architecture, a cost model, and a falsifiable milestone.

quantpi · service/telemetry
$ service.describe()
deliverable: roadmap + reference architecture + cost model
ip transfer: complete · lock-in: none
delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
SYS/01The problem

The expensive failure mode isn't bad AI. It's the wrong project.

Enterprises don't usually fail at AI because the model underperforms — they fail by selecting initiatives with no data foundation, no owner, and no measurable baseline. Selection is a strategy problem, and it's solvable with engineering discipline applied before any code exists.

SYS/02What we build

Capabilities

Opportunity audit

We score your candidate use cases on data readiness, integration cost, and value density — and tell you, explicitly, which candidates to retire.

output: ranked portfolio

Build vs buy vs wait

Honest analysis including ‘wait’ — sometimes the right move is letting the ecosystem mature six months. We model all three paths with costs.

bias: none — we build and buy

Reference architecture

Target-state architecture grounded in your actual estate: identity, data platforms, network topology, compliance constraints.

format: ADRs + diagrams

Unit economics modeling

Cost per request, per document, per decision — modeled before you commit. AI initiatives die of unit economics more than accuracy.

model: per-transaction

Governance & risk frame

EU AI Act classification, model risk tiers, human-oversight design — built into the roadmap, not bolted on at audit time.

frame: regulation-aware

Executive alignment

Board-ready material that survives technical scrutiny, because the people writing it can defend every line.

audience: board + builders
SYS/03How we work

The approach

A sequence, because the order is the point: each phase gates the next on evidence.

01 /

Discover

Two weeks of structured interviews, data-estate review, and candidate use-case collection across your org.

02 /

Score & select

Each candidate scored on value, feasibility, data readiness, and risk. You get a ranked portfolio and explicit kill recommendations.

03 /

Blueprint

Reference architecture, unit-economics model, and governance frame for the top initiatives — detailed enough for an engineering team to start.

04 /

Roadmap & handoff

A sequenced 12-month plan with falsifiable milestones, budget envelopes, and decision gates. Optionally, we build phase one.

SYS/04What you receive

Deliverables

  • Scored and ranked AI opportunity portfolio
  • Build/buy/wait analysis with cost models
  • Target-state reference architecture
  • Unit economics model per initiative
  • Governance and risk classification frame
  • 12-month sequenced roadmap with gates
  • Executive presentation pack
  • Optional: phase-one delivery proposal
Working stack
TOGAF-alignedAzure Well-ArchitectedAWS Well-ArchitectedEU AI ActNIST AI RMFISO 42001
SYS/05Questions, answered straight

FAQ

How is this different from Big-4 AI strategy?
Our strategists are the engineers who will be accountable if you ask us to build phase one. That changes incentives: we can't recommend things we can't deliver, and every estimate is one we'd accept as a budget.
How long does a strategy engagement take?
Four to six weeks for the full cycle — discovery, scoring, blueprint, roadmap. Tightly scoped questions (a build-vs-buy decision on one initiative) can be answered in two.
What if we already have an AI roadmap?
Then we pressure-test it. A roadmap review — feasibility, sequencing, unit economics, missing risks — takes two weeks and routinely saves clients from one or two doomed initiatives.
Do you only recommend technologies you implement?
No, and we recommend buying over building more often than you'd expect from an engineering firm. The analysis includes commercial tools, open source, and incumbent platforms you already license.

Ship AI that earns its place in production.

Tell us what you're building. We'll tell you, candidly, how we'd build it — architecture, timeline, and cost.

Average first response: under 24 hours · straight engineering answers, no pitch theatre