AI experts, embedded in your team. Ship production AI faster.
Every AI deployment needs the same four roles: Architect, Systems, Application, Platform. We bring them on a fractional basis, working alongside your engineers who learn the role by doing it. Your team ships the work. Your bench grows.
4 AI expert roles
Architect · Systems · Application · Platform
Fractional
By the month, from 5M FCFA per month
Embedded
Every expert works alongside engineers on your team
Production bar
Only senior engineers who have shipped in production
The senior AI expertise most enterprises cannot hire.
Senior AI talent is expensive, scarce, and rarely available at enterprise salary bands. Meanwhile, your AI initiatives are stalling because the senior judgment is missing, not the ambition.
AI Experts gives you fractional access to the four roles every AI deployment actually needs. Each expert embeds alongside your team, works with your engineers, and stays as long as the work requires. When the work is done, your team has both the result and the skills to run it.
One bench
One bench. One bar. One approach.
Build & Learn and AI Experts draw from the same bench, hold the same bar, and follow the same collaborative delivery approach. Only the delivery shape changes.
Same bench
Senior AI architects and engineers from the same bench as Build & Learn. No juniors, no passengers, no hand-offs.
Same approach
Your team writes the code. Our experts guide, review, and work alongside. Collaborative delivery is contractual, not optional.
Same outcome
Your AI initiative ships, and your team leaves capable of owning what the expert helped build.
The four experts
Every AI deployment needs the same four roles.
Architect, Systems, Application, Platform. Deploy one, deploy all four. Each works in sequence and in parallel, each embedded alongside members of your team.
AI Architect
The senior voice who decides what to build, what to buy, and what to retire across your AI portfolio.
What this role owns
- Architecture decisions that hold up under real traffic, not demo data
- Build-vs-buy calls on models, vendors, and infrastructure with the budget math to back them
- Technical standards your internal team and your vendors actually follow
- The judgment most enterprises cannot hire full-time
The pain this role solves
Most enterprises run three to five AI initiatives with no shared architecture. Vendors over-sell, internal teams under-scope, and the platform team inherits the mess. This is the role that brings adult supervision to your AI portfolio.
Stack
System design · Model selection · Vendor evaluation · Portfolio governance · Cloud architecture (AWS / Azure / GCP) · Cost modeling · Technical due diligence
AI Systems Engineer
The role that builds, trains, and evaluates the models themselves, rigorously and on the data you actually have.
What this role owns
- Models trained on your data, evaluated on your business metrics, not benchmark scores
- Data pipelines that hold up when the data shifts, because it will
- Experiment tracking and model versioning a regulator could audit
- The rigor that separates “it worked once” from “it works in production”
The pain this role solves
Most AI work in the region stops at the first working notebook. This is the role that takes it to the bar a regulator, a central bank, or a board will accept, with proper evaluation, versioning, and data discipline.
Stack
PyTorch · TensorFlow · Hugging Face · scikit-learn · XGBoost · MLflow · DVC · Apache Airflow · pandas / Polars · Experiment tracking
AI Application Engineer
The role that turns a model into a product your customers and your operators actually use.
What this role owns
- User-facing AI features with real UX, not notebook demos
- Agent and RAG systems that hold up in production, with evaluations, guardrails, and cost discipline
- LLM orchestration that survives an audit from security and compliance
- The integration work that connects the model to the workflow it has to change
The pain this role solves
Enterprises buy LLM platforms without anyone on staff who has shipped a production agent. The result: proof-of-concept chat demos that do not survive the first compliance review. This is the role you need on your side of the table before you buy the platform.
Stack
LangGraph · LangChain · LlamaIndex · OpenAI / Anthropic APIs · Vector databases (Pinecone, Weaviate, pgvector) · Eval frameworks (Ragas, LangSmith) · Python · TypeScript · React
AI Platform & Production Engineer
The role that turns a model in a notebook into a model serving real traffic at real cost.
What this role owns
- Models in production at target latency and target cost, not just “deployed”
- Monitoring, alerting, and cost observability from day one
- Inference infrastructure that survives when traffic triples
- The MLOps discipline most AI pilots never reach
The pain this role solves
Most AI pilots never reach production. Not because the model did not work, because no one owned deployment, monitoring, and cost. This is the role that closes that gap. It is also the role your bench does not have.
Stack
Kubernetes · Ray / Ray Serve · BentoML · MLflow · Triton Inference Server · Prometheus / Grafana · Terraform · AWS / GCP ML infrastructure
How we deploy
Three disciplines that separate us from a staffing firm.
Every expert works alongside your engineers.
No embedded team, no engagement. Every Edacy expert works alongside engineers on your team who are learning the role by doing it. When we leave, your team owns the skill, not just the artifact.
Every engagement has a delivery milestone.
We name a production milestone at scoping: latency X, cost Y, live by week Z. Scope is defined. Outcomes are measurable. No open-ended staff augmentation disguised as consulting.
Your team owns the code.
At the end of the engagement, the code that runs your business is code your team helped write and understands line by line. We architect, we review, we work alongside. The repository is yours.
Pricing
Fractional by role. Transparent by default.
Per-role pricing that scales with the work. Most engagements run 3–9 months. Multi-role and multi-month engagements include volume discounts.
Single role
From 5M FCFA / month
One embedded AI expert
A senior expert from our bench, deployed into your team. Ideal for unblocking a stalled project or adding specialized seniority.
- One named senior expert
- Embedded with your team
- Monthly cadence, minimum 3 months
- Scoped delivery milestone
Two roles
From 9M FCFA / month
Two complementary experts
Two roles working in tandem. Common pairings: Architect + Platform, or Systems + Application.
- Two named experts
- Embedded with your team
- Coordinated delivery milestone
- Shared architecture review cadence
Full stack
From 18M FCFA / month
All four roles, embedded
Full AI production stack embedded alongside your team. Best for enterprises standing up a new AI practice end to end.
- All four named experts
- Embedded with your team
- Portfolio-level architecture governance
- Multi-initiative coordination
The bar
Every Edacy expert has shipped production AI.
No junior hires. No bootcamp graduates. No passengers. The bench is small, and intentionally so.
10+ years
Median architect experience
In applied AI and enterprise system design
6+ years
Median platform engineer experience
In production ML infrastructure
100%
Bench shipped to production
Every expert has owned a model running live for 12+ months
Frequently asked
Questions, answered.
Build & Learn is a structured 8–16 week engagement where your team writes 100% of the code with expert coaching. AI Experts is fractional: our experts embed alongside your team, work on real delivery with your engineers, and engage by the month. Build & Learn optimizes for capability transfer. AI Experts optimizes for speed to production, with capability transfer happening through collaborative delivery.
No. Every Edacy expert works inside our bench, across multiple engagements, staying sharp on what it takes to ship production AI. Pulling one out for a full-time hire breaks that. If you need a permanent hire, we can introduce you to candidates from our alumni network at no placement fee.
Yes. Many engagements start with just an Architect for strategy work, or a Platform Engineer to unblock a stalled deployment. We will tell you honestly if a single role is enough or if the work needs a second role to ship.
Yes. During scoping, we introduce you to the named expert who will run the engagement. You will know exactly who you are working with before you commit.
Yes. Every Edacy expert works alongside engineers on your team. Collaborative delivery is contractual, not optional. The learning happens through real delivery instead of structured coaching, which suits some teams better.
Those firms place individual engineers into your team on a staff-augmentation basis. We deploy a named expert in a defined role, embedded with your team and with a shared delivery outcome. Different product, different buyer. If you need generalist engineers for twelve months, they are the right call. If you need a senior AI specialist to unblock an initiative and grow your team's capability, we are.
Tell us during scoping. We will help you identify the right people internally. If you genuinely have no one for our experts to work alongside, we will tell you honestly that we are not the right fit, and we will refer you to firms that do pure delivery work.
Which expert does your team need first?
Tell us what is stuck, what is missing, or what you are trying to build. We will come back with the right role and a scoped delivery milestone.