Regulated bank · 8-week AI roadmap
From 47 loose ideas to 6 prioritized initiatives with TCO, production gates and a governance plan approved by legal. Approved by exec committee on the first presentation.
Where to invest in AI — and how to measure return.
For C-levels who need to make AI decisions with confidence. We bring the experience of taking models from hypothesis to production in regulated markets.
What we do
AI decisions, with engineering rigor.
We don't recommend a tool — we recommend an architecture, a path, a cost. All anchored in testable hypotheses.
Readiness diagnostic
Opportunity map, potential value per use case, data and culture readiness. In 2–3 weeks.
Prioritized roadmap
12–24 month sequence of initiatives. ROI, dependencies, internal vs. partner capacity.
Reference architecture
Model, data, security, observability, cost. Vendor-neutral, fitted to your existing stack.
Governance and compliance
LGPD frameworks, model-cards, audit, risk management. Ready for legal and the board.
Executive enablement
Workshops for C-level and technical teams. Build vs. buy, paper reading, model selection.
Continuous AI committee
Recurring senior seat. Monthly review of initiatives, critical choices, course correction.
Positioning
AI is not a department. It's a cross-cutting capability.
Companies that treat AI as an isolated project deliver demos. Those that treat it as a capability — with governance, metrics and decision cadence — deliver systems. The difference between the two stances costs 6 months to 3 years of competitive advantage.
By the numbers
What weighs in when the roadmap reaches the committee.
0+diagnostics delivered
0%of initiatives reach production within 12 months
0-3 wksfrom kick-off to prioritized roadmap
NPS 0average among executive sponsors
How we deliver
Diagnostic → roadmap → continuous governance.
Four stages that compress 12 months down to 3-4 between first conversation and first KPI-attached production initiative.
- C1
Opportunity map
Workshops with C-level, key business areas and existing data. We identify use cases by potential value × readiness.
Output Prioritized list of 8-12 initiatives with TCO.
- C2
Reference architecture
Model, data, security, observability and cost. Vendor-neutral, fitted to existing stack.
Output Executable diagram + decision document.
- C3
Governance and gates
LGPD/regulatory frameworks, model-cards, audit and production gates. Approved by legal before first POC.
Output Versioned governance playbook.
- C4
Continuous committee
Recurring senior seat reviewing initiatives, critical choices and course corrections.
Output Monthly cadence with logged decisions.
In production
Where advisory has moved the needle.
Multinational manufacturer · reference architecture
Vendor-neutral stack with data layer, observability and model-agnostic. Accelerated the 2nd and 3rd use case because the technical foundation was already in place.
B2B fintech · continuous AI committee
Recurring senior seat replaced three different vendor committees. Build-vs-buy decisions, model choice and incident review now happen in a single forum.
Vs. alternatives
Why not big-four consulting?
LA AIAI specialist
Traditional consultingBig-4, generalists
Internal teamNo external support
Applied AI focus (not generic advocacy)
Architecture battle-tested in production
Board-ready compliance documentation
Vendor-neutral (no resale incentive)
Commitment until production, not until deck
Average time to first measured KPI
3-4 months
9-18 months
6-12 months
Frequently asked
Before kick-off.
How is LA AI different from a traditional tech consultancy?
Traditional consultancies hand over a deck and move on. LA AI delivers tested architecture, a roadmap with gate criteria, and stays accountable until the system is in production with a business metric attached.
How long does a diagnostic take?
2–3 weeks, depending on company size and how many use cases enter the initial scope. Deliverable is an executive document + validation workshop with C-level.
Do you work with any LLM?
Yes. We are vendor-neutral: OpenAI, Anthropic, Google, Mistral, self-hosted open-source models and proprietary fine-tunes. The choice comes from the diagnostic, not from prior preference.
Does it make sense to hire advisory before a POC?
In nearly every case, yes. POCs without a diagnostic become demos that don't reach production — see our post on this. Diagnostic cost is usually a fraction of what's wasted on POCs that don't go live.