One AI solution deployed. A team ready to ship the next.
Build & Learn is a structured 8–16 week engagement where your engineering team ships a production AI system, guided by senior AI architects and technical coaches. The result is a working product and a team ready to deliver the next one on their own.
A delivery model designed for capability transfer.
Most approaches to enterprise AI force a trade-off. Train your team, and you wait months before anything gets built. Hire a vendor, and you get a product but no internal capability.
Edacy removes that trade-off. Your engineering team builds a production AI system from the first sprint. Our experts guide the technical direction, review every piece of work, and teach the specific skills your team needs, not in a classroom, but inside the project itself.
“The result: a working product and a team that knows how to build the next one.”
Three interlocking cycles. Every week.
The Build Cycle
Every week, your engineers work on real sprint deliverables: building data pipelines, training models, wiring API endpoints, or deploying AI features. This is not a simulation. The work ships.
A dedicated technical coach reviews every pull request with detailed feedback: what's working, what should change, and why. Same-day async support is available when the team gets stuck.
The Learn Cycle
Two to three live instructor-led sessions each week, timed to match what the team is building in the current sprint.
70% Shared Sessions
Cover foundational topics across all active client teams: AI and ML fundamentals, prompt engineering, data pipelines, MLOps, and governance.
30% Custom Sessions
Your technical coach leads project-specific workshops using your own codebase and data, covering the exact challenges your engineers face that week.
The Review Cycle
Every two weeks, an AI architect reviews the full state of the project. This is not a status meeting. It is a deep technical review.
- Assesses architecture decisions and code quality
- Teaches deeper concepts the team struggled with during the sprint
- Provides direct, constructive feedback
- Adjusts the roadmap and next sprint plan based on progress and priorities
For example: if your team built a data pipeline that works but won't scale, the architect explains why, redesigns the approach, and scopes the fix into the next sprint.
Four solution tracks. One delivery model.
Every Build & Learn engagement follows the same approach. Pick the track that matches the outcome you need.
Data Foundations
Build the data layer AI depends on: pipelines, warehouses, and dashboards your team owns.
Applied ML & AI
Improve forecasting, scoring, and operational decisions with models your team builds and maintains.
GenAI & LLM Applications
Automate knowledge work and workflows: RAG systems, AI agents, and intelligent tools your team ships.
AI Infrastructure
Run AI reliably in production: MLOps, monitoring, and governance your team operates.
Built for leaders who need outcomes and capability.
For CTOs & VP Engineering
- Ship a production AI use case with your current engineers
- Build lasting AI delivery skills inside your team
- Reduce dependency on external vendors for future AI work
- Prove your team can deliver AI with evidence, not theory
For Heads of L&D & HR
- Applied capability building, not passive learning
- Measurable skill development through live delivery
- Stronger retention and internal mobility
- Workforce transformation aligned with business priorities
For CEOs & Business Leaders
- Go from AI strategy to a working product in 16 weeks
- Close the gap between strategy and implementation
- Accelerate operational value from AI investments
- Build lasting internal capability, not one-off vendor delivery
What leaders get during the engagement.
Edacy is not a black box. Enterprise sponsors get structured visibility into both delivery progress and capability growth.
Biweekly progress reviews
Clear updates on what was built, what the team learned, and what comes next, structured for leadership, not just engineers.
Visibility into risks and blockers
Architects flag delivery risks early and provide actionable recommendations so leadership can make informed decisions.
Evidence of capability growth
Track how your team's AI skills are developing sprint by sprint, measured through actual deliverables, not test scores.
Milestone-based reporting
Every engagement follows a defined milestone structure so progress is measurable and accountable.
Scale recommendations
At the end of the engagement, we recommend whether to scale, extend, or move to the next use case, based on evidence.
Team contribution metrics
Understand what your team is expected to contribute in time and effort, so you can plan around delivery commitments.
Who guides your team.
AI Architect
Designs the overall system architecture. Creates the sprint roadmap. Leads biweekly reviews. Does not write code. Focuses entirely on direction, quality, and teaching.
Technical Coach
Your team's primary point of contact. Reviews every pull request with detailed instructive feedback. Leads live sessions. Runs checkpoints and manages day-to-day support.
Fundamentals Instructor
Runs the shared weekly sessions covering core AI topics. Delivers consistent, high-quality foundational content across all active engagements.
The principle: your team does the work.
This is not outsourcing with a learning layer. Your engineers write the code. They work with your data. They make architecture decisions, guided by our architects, not dependent on them.
We set the technical direction, structure the delivery, review the output, and teach what's needed at each stage. Skills transfer because your team is the one building.
100%
of code written by your engineers
100%
of intellectual property stays with you
Your
production data and business problems
Zero
dependency on external teams after engagement
“When the engagement ends, you have a shipped AI product and a team that knows how to build the next one. No ongoing dependency.”
Three ways to engage.
Start with a single project, commit to a long-term partnership, or begin with a readiness assessment. Each model uses the same Build & Learn delivery approach.
Sprint Engagement
From 10M FCFA
One team. One project. The fastest way to prove the model and deliver a working AI product.
- Dedicated technical coach
- Biweekly architect reviews
- 2–3 live sessions per week
- PR reviews with coaching feedback
Annual Partnership
From 30M FCFA
Multiple rolling projects with priority access to architects and leadership briefings.
- Multiple concurrent projects
- Priority architect access
- Quarterly leadership briefings
- Volume-based pricing
AI Readiness Assessment
From 5M FCFA
A structured evaluation of your team, data, and use cases. The right starting point if you need clarity before committing.
- AI readiness assessment
- Team capability audit
- Use case prioritization
- Actionable recommendations
Frequently asked
Questions, answered.
Mid-to-large enterprises with an engineering team who want to build an AI capability, not just buy an AI product. You should have at least 3 to 6 engineers available to participate in the sprint, and a real business problem worth solving with AI.
No. Build & Learn is designed to take strong software engineers and make them production AI-capable. What matters is engineering fundamentals, not prior AI experience.
Build & Learn is a structured 8 to 16 week engagement where your team writes 100% of the code under expert guidance. AI Experts is fractional: senior experts embed alongside your team and work on the code with them, by the month. Build & Learn optimizes for capability transfer. AI Experts optimizes for speed.
Most projects fit inside 8 to 16 weeks when properly scoped. For enterprises with multiple rolling projects, we offer Annual Partnership with priority architect access and volume pricing.
Your engineers do. 100%. Our architects design the system, our coaches review every pull request, run live sessions, and teach inside the project. But the code your business runs on is code your team wrote and understands line by line.
Daily async support, same-week architect escalation, and scope renegotiation if needed. We name a production milestone at scoping and we either hit it or we tell you early enough to change course.
Yes. Some clients add a fractional specialist (Platform or Systems) during a Build & Learn sprint to accelerate a specific piece of the work. It's quoted separately and scoped together.
Ready to ship a production
AI system with your team?
Book a strategy call and we will help you scope the right track, engagement model, and starting point for your team.