Five phases, with the same engineers from kickoff to handoff.
Below is the full version, which is our AI Enablement Sprint. Rapid AI Development skips Sprint Zero and goes straight to architecture and code. Fractional engagements run the same discipline at a steady pace.
The AI Enablement Sprint
12 weeks
Rapid AI Development skips Sprint Zero and goes straight to architecture and code.
Fractional AI Team runs the same discipline at a steady pace.
The engagement
Roughly one quarter from kickoff to handoff
Pick a phase to see what happens, week by week, and what you have at the end of it.
Phase 1 · Week 1–2
Sprint Zero
We interview stakeholders, map your processes and data flows, and decide where the PII boundaries sit. Then every candidate use case goes through the AI Necessity Gate, which asks three questions.
Test 1
The Simplicity Test
Does a deterministic pipeline do this?
Test 2
The Clarity Test
Is the outcome measurable in value, not hours?
Test 3
The Data Test
Is the data governed enough to trust the output?
The team
Who you'll actually work with
Every engagement is staffed by a small group of senior people, and you'll work with the same people from kickoff to handoff.
Engagement Lead
Strategy & delivery
Aligns with your leadership on roadmap, ROI, and decision cadence. Owns the executive relationship and the SOW.
Where they spend their timeWeekly leadership syncs, scope adjustments, value validation.
Principal Architect
Systems & Data Architect
Designs the system architecture: data flows, model routing, security boundaries, RAG pipelines, vector DBs, data contracts and metadata.
Where they spend their timeArchitecture sessions with your engineering and security teams.
Senior AI Engineer
Agents & orchestration
Hands-on engineer. Builds the agents, configures orchestration (LangGraph, LangChain, MCP), implements human-in-the-loop workflows.
Where they spend their timeIn your codebase, in production logs, in the weekly demos.
Adoption Lead
Training & change
Builds a group of internal champions, designs training for each role, manages internal comms so the system actually gets adopted.
Where they spend their timeWith your product owners, in training sessions, in runbook authoring.
Senior Data Engineers
Pipelines & evaluation
Build the semantic layer, embeddings, ingestion pipelines, APIs, MCP integrations, and the evaluation frameworks that keep the system honest.
Where they spend their timeIn your data warehouse, building APIs, on integration testing.
Our leads pair directly with your security, platform, internal engineering, and privacy organizations. We work alongside your team rather than around it.
Cross-cutting
Security is part of every phase
We don't run security as a separate workstream. Here's where each piece gets done.
FusionLeap engineers to your compliance team's spec. We do not provide legal, regulatory, or audit opinions, and we do not act as your DPO, counsel, or auditor of record.
Secure-by-design
How we secure AI-assisted development
The AI coding tools we use bring their own risks: leaked IP, exposed secrets, and insecure code. Before any code gets written, we lock down the development environment in four layers.
Configuration
Every AI coding assistant runs on enterprise settings: no training on your code, sensitive files excluded, telemetry locked down.
Code Security
Secret scanning, SAST tools such as Snyk and SonarQube, and pre-commit hooks such as Gitleaks catch credentials and insecure patterns before anything is merged.
Network
For clinical records, pet-owner PII, and payments, AI traffic goes through private endpoints such as Azure OpenAI, AWS Bedrock with VPC isolation, or your own model gateway.
Culture & Governance
Your engineers learn to review AI-written code more closely than human-written code, so a person still makes the final call.
Where AI coding tools help, and where they don't
Green zone
High AI autonomy
Unit tests · Documentation · Boilerplate · New microservices
Yellow zone
Human pairing required
Legacy refactoring · API integration · SQL optimization
Red zone
Human-led
Proprietary internal libraries · System-wide architecture · God classes
AI models learn mostly from modern public code, so on a decade-old monolith or a proprietary SDK they write code that looks right but breaks rules nobody wrote down. Architecture decisions and cross-service dependencies still need a person.
Who does what
FusionLeap vs. your team
Procurement always asks: "What does my team have to do?" and "How many hours per week?" Here's what to expect.
Phase 0: Pre-engagement
FusionLeap
Mutual NDA, security questionnaire response, MSA/SOW drafting, kickoff agenda, sub-processor disclosure
Your team
Sponsor + security/legal reviewers identified, stakeholders mapped, system access scoped
Phase 1: Sprint Zero
FusionLeap
Stakeholder interviews, process and data-flow mapping, PII boundary design, AI Necessity Gate, build plan
Your team
Make 8–15 stakeholders available (1 hr each), provide read-only data access, share strategic context
Phase 2: Rapid prototyping
FusionLeap
Architecture, environment setup, model selection, build, weekly demos, cost analysis
Your team
Decision-maker at weekly demo (1 hr/wk), data SME available (3–5 hrs/wk), feedback within 2 business days
Phase 3: Production hardening
FusionLeap
Error handling, observability, audit trails, cost ceilings, integration tests, security validation memo, executive demo
Your team
Test data sign-off, executive sponsor at hand-off demo, security team available for review
Phase 4: Enablement
FusionLeap
Runbook, model selection criteria, fallback policies, cost ceilings, training sessions, on-call shadowing
Your team
Engineering team participates in training, product owner identified for the ongoing system
Post-engagement
FusionLeap
Check-ins 4 weeks after handoff and at the end of the first quarter. Optional Fractional AI Team engagement
Your team
System ownership, on-call coverage, regular quarterly reviews
Senior client time per week
Hours, across the 12-week engagement. Hover a week for detail.
Show as table
| Weeks | Senior client time |
|---|---|
| 1–4 | 6–10 hours/week |
| 5–12 | 3–5 hours/week |
Senior client time
A few hours a week
More in the first four weeks, less after that
Concentrated in three roles
- Executive sponsor for governance2–4 hrs/wk
- Data SME for build feedback3–5 hrs/wk
- Engineering team during Phase 4 handoff5–8 hrs/wk
Principles
What stays the same on every engagement
Senior people only.
We don't staff juniors on client work.
Your data, from week three.
We work with your real data rather than sanitized samples, tokenized where it's sensitive and kept inside your perimeter.
Security from the start.
PII boundaries, model logging, audit trails, and review policies are designed up front, before the build starts, so they don't surface late in review.
We'll tell you when AI is the wrong tool.
If a deterministic script does the job better than an LLM, we build the script. We recommend against AI about as often as we recommend it.
You own it.
Everything runs in your cloud, on your keys, in your repos. You aren't locked in to us or to a model provider.
We plan to leave.
Runbooks, shadowing, and pairing mean your team can run the system on its own after week twelve.
Ready to run an engagement like this?
In a 30-minute call we'll help you work out which engagement fits and what the first 90 days would look like.
Talk to an Architect