The workforce is ready
60% of technology professionals want skills-based organizations; 78% of senior leadership call the transition strategic [TI Inside, Aug 2026].
Your domain moves daily. The generic model is the bottleneck.
Squads that speak your domain — trained on your data, tuned to your unit economics, supervised by your people.
Why Studio
The flagship: legacy translation
Akana Studio translates rigid legacy logic — SOAP APIs, mainframe syntax — into standardized intents via specialized Small Models, with continuous learning and rigorous QA against institutional guidelines.
What we commit to measure
Latency per workflow. Cost per delivered unit of work. Domain accuracy vs. your current prompts. Measured per engagement — not promised.
What the Team Gains
- When the model forgets your schema weekly, I want agents trained on our Knowledge Bases, so answers stay grounded in our sources, not re-guessed from a prompt window.
- When every team re-invents the same prompts, I want one governed pipeline with QA against institutional guidelines, so context becomes an asset, not folklore.
- When token costs scale with generic models, I want small models tuned to our unit economics — measured, not promised.
- When regulated data enters training, I want the governance gates bound before a single epoch runs, so compliance is a precondition, not a post-mortem.
The Context Problem
Your best agent answers the same question differently every week — it re-reads the wiki, re-learns the table schema, re-guesses the legacy field names. Your knowledge lives in tickets, contracts, SOAP services, and schemas. Prompt windows are not an integration layer.
Built on this layer
Studio Competitive Analysis
| Context Operational Feature | Generic LLM + Prompts | Akana Studio |
|---|---|---|
| Domain | Generic context window, re-prompted daily | Knowledge Bases + SML trained on your data |
| QA | Prompt roulette per team | Continuous learning with rigorous QA against institutional guidelines |
| Economics | You rent their scale, pay their tokens | Small models tuned to your margins — measured per engagement |
Sketch a squad
Pick context, workflow, constraints — the manifest writes itself. Governance always ships.
01 Context sources
Where your knowledge lives — select all that apply
02 Workflow
One workflow per squad
03 Constraints
What the gates must enforce
# Akana squad manifest — generated at akana.cloud/studio name: your-first-squad knowledge_bases: [] # pick context sources above agents: [] # pick a workflow above governance: gates: [review, audit-trail] private_ai: true # deploy: akana deploy --squad <workflow>
The AI Governance Assessment
10 scenario questions. About 3 minutes. Results weighted to what is at stake for your operation — with a plain-language action plan for your two weakest dimensions.
- Oversight & Supervision
- Data Control & Sovereignty
- Auditability & Compliance
- Cost & Spend Visibility
- Execution Discipline
First squad: one workflow tuned on one knowledge base
30 days — measured on domain accuracy vs. your current prompts.
Domain accuracy
Tuned squad vs. your current prompts, same questions
Knowledge coverage
Share of answers grounded in your sources
Unit economics
Cost per delivered unit of work, tracked weekly