Know what AI is worth to your business before you write a line of code.
We help leadership teams decide where to invest, what to build, and what to defer. Strategy work that's grounded in your data and ready to ship.
- Typical duration
- 4–8 weeks
- Squad
- 1 strategist, 1 senior engineer
- Starting at
- $18,000 fixed scope
- Time to a funded roadmap
- 6 weeksTime to a funded roadmap
- Use cases screened per engagement
- 30+Use cases screened per engagement
- Client-reported ROI within 12mo
- 4.6xClient-reported ROI within 12mo
Most AI strategy decks never survive contact with production.
The gap between an AI roadmap and a shipped system is where budgets die. Slideware built by people who have never deployed a model underestimates data readiness, overestimates model capability, and ignores the compliance work entirely. You end up with a mandate, a number, and no path.
Sound familiar?
- A board mandate for AI with no defensible business case
- Pilots that demo well and never reach production
- Vendor quotes that vary by 10x with no way to compare them
- No shared definition of what 'done' or 'working' means
How we deliver ai consulting.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Assess
Two weeks inside your data, systems, and workflows. We interview operators, read the schemas, and score readiness across data, tooling, talent, and governance.
Prioritize
Every candidate use case scored on value, feasibility, and time-to-first-value. We kill the ones that won't work and tell you why.
Model
A defensible ROI model per initiative — with the assumptions written down, the sensitivity ranges visible, and the break-even date named.
Sequence
A quarter-by-quarter roadmap with build-vs-buy calls, staffing shape, and the platform investments that unlock everything downstream.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
AI Maturity Assessment
A scored readiness baseline across data quality, platform tooling, in-house talent, and governance — benchmarked against peers in your sector.
Use-case portfolio scoring
Every candidate initiative ranked on expected value, technical feasibility, and dependency depth, so sequencing arguments end with evidence.
Build-vs-buy analysis
Honest total-cost-of-ownership comparisons, including the maintenance burden vendors leave out of their proposals.
ROI & sensitivity modeling
Financial models your CFO will accept — assumptions itemized, ranges stated, and the downside case modeled alongside the upside.
Vendor selection
Structured RFP support: evaluation rubrics, reference-call scripts, and technical due diligence on the claims in the deck.
Governance design
Model risk policy, approval gates, and audit posture designed to satisfy your regulator before the first deployment, not after.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Python
- dbt
- Snowflake
- Claude
- OpenAI
- Metabase
What this looks like in production.
Grounding an AI research analyst on a decade of data
Atlas Capital
- Problem
- Analysts spent 60% of their day searching filings, transcripts, and broker notes for context.
- Solution
- A citation-grounded RAG system with role-aware controls, evaluated against a 1,200-question benchmark.
- Outcome
- Analyst throughput doubled on coverage tasks and onboarding time for new hires dropped by half.
- Analyst throughput
- 2xAnalyst throughput
- Onboarding time
- −50%Onboarding time
- Answer grounding
- 97%Answer grounding
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.