FusionLeap Digital
FusionLeap Digital
Methodology

How we run an engagement

The same engineering discipline runs through every engagement: discover the use case (when needed), build it on your data, harden it for production, hand it to your team. Below is the canonical 10–12 week version — our AI Enablement Sprint. Rapid AI Development compresses it. Fractional engagements adapt it to a sustained cadence.

The engagement at a glance

Five phases. Twelve weeks. One promise.

Phase 0 · Before kickoff

Pre-Engagement

Signed MSA + SOW, security review complete

Phase 1 · Week 1–2

Discovery & Mapping

Use case scoped, data flows mapped, requirements signed off

Phase 2 · Week 3–7

Rapid Prototyping

Working prototype on your data, weekly demos, cost analysis

Phase 3 · Week 8–10

Production Hardening & Demo

Error handling, observability, executive hand-off demo

Phase 4 · Week 11–12

Enablement & Knowledge Transfer

Your team running the system, runbook in hand

The Launch Team

Who you'll actually work with

We deploy a thin slice of principals on every engagement — no juniors, no learning on your dime. You'll work with the same five people from kickoff to handoff.

Chief Strategist

Engagement Lead

Aligns with your leadership on roadmap, ROI, and decision cadence. Owns the executive relationship and the SOW.

Where they spend their time

Weekly 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 time

Architecture sessions with your engineering and security teams.

Agentic Builder

Senior AI Engineer

Hands-on engineer. Builds the agents, configures orchestration (LangGraph, LangChain, MCP), implements human-in-the-loop workflows.

Where they spend their time

In your codebase, in production logs, in the weekly demos.

Fluency Guide

Organizational Change Lead

Runs the champion network, designs persona-based training, manages internal comms so the system actually gets adopted.

Where they spend their time

With your product owners, in training sessions, in runbook authoring.

Senior Data Engineers

Senior + Mid-Level

Build the semantic layer, embeddings, ingestion pipelines, APIs, MCP integrations, and the evaluation frameworks that keep the system honest.

Where they spend their time

In 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 — not around them.

Phase 0: Pre-Engagement

Before kickoff · typically 2–4 weeks

What happens: The procurement and legal-readiness work that mid-market and enterprise buyers need before any engineering can start. We surface this up front so it doesn't derail Week 1.

Activities:

  • Mutual NDA and discovery call
  • Security questionnaire response (CAIQ Lite or equivalent)
  • MSA + SOW drafting and execution
  • Sub-processor and dependency disclosure
  • Sponsor, decision-maker, and reviewer identification
  • Kickoff agenda and stakeholder list

Outcomes:

  • Signed MSA + SOW
  • Security review complete
  • Sponsor + RACI assigned
  • Kickoff date confirmed

Phase 1: Discovery & Mapping

Week 1–2

What happens: We embed with your team to understand what you're actually trying to accomplish — and to map the data and systems we'll touch.

Activities:

  • Stakeholder interviews (8–15 across functions)
  • Current process mapping and pain point identification
  • Data landscape, classification, and flow mapping
  • Technology stack review and integration points
  • AI readiness scoring

Deliverables:

  • AI Opportunity Roadmap with ROI estimates
  • Readiness Assessment
  • Recommended Approach
  • Technical Requirements Doc
  • Data flow diagram (security artifact starts here)

Phase 2: Rapid Prototyping

Week 3–7

What happens: We build a working AI solution using your actual data, with weekly demos and continuous feedback.

Week 3–4: Architecture & Setup

  • Solution design (models, approach, tools)
  • Development environment setup in your accounts
  • Data pipeline creation with PII boundaries
  • Model selection and initial testing

Week 5–6: Iterative Development

  • Core functionality build
  • Weekly demos with stakeholders
  • Refinement based on feedback
  • Cost optimization and model routing

Week 7: Polish & Prepare

  • Edge case handling
  • User interface (where needed)
  • Technical documentation
  • Hand-off into Phase 3 hardening

Deliverables:

  • Working Proof of Concept (production-quality code)
  • Model comparison report
  • Cost analysis and projected unit economics
  • Technical documentation

Phase 3: Production Hardening & Demo

Week 8–10

What happens: By Week 8 your prototype works. Phase 3 is where we harden it for production — error handling, model fallbacks, observability, cost controls, integration tests against your real data, and a hand-off demo to the executive sponsor. This is where most consultancies stop. We start.

Week 8: Hardening

  • Error handling and graceful degradation
  • Model fallback strategy and retry logic
  • Observability: logs, traces, metrics, alerts
  • Cost controls: per-request ceilings, budget alerts

Week 9: Integration testing & security validation

  • Integration testing against production data (read-only or sandboxed)
  • Security team review of audit trails and access controls
  • Reviewer-in-the-loop checkpoints validated
  • Load and concurrency testing

Week 10: Hand-off demo

  • Executive sponsor demo on production-equivalent data
  • Go/no-go decision for production rollout
  • Production deployment plan and rollback procedure

Deliverables:

  • Production-hardened system
  • Observability dashboard with alerting
  • Security validation memo for your security team
  • Production deployment + rollback plan

Phase 4: Enablement & Knowledge Transfer

Week 11–12

What happens: Your team takes ownership. We don't leave behind a black box — we leave behind a runbook, model selection criteria, fallback policies, and cost ceilings your engineers can defend.

Week 11: Team enablement

  • Walkthrough of architecture, models, prompts, and decision boundaries
  • Model selection criteria and routing logic explained
  • Fallback policies and degraded-mode operation
  • Cost ceilings, budget alerts, and routing-by-cost configured

Week 12: Production handoff

  • On-call shadowing with your engineering team
  • Incident runbook walkthrough
  • Post-engagement check-in scheduled (Week 16, Quarter 1)
  • Optional Fractional retainer scoped (if continuing)

Deliverables:

  • Your team operating the system independently
  • System runbook (model criteria, fallback policies, cost ceilings)
  • Cost controls in production (token tracking, budget alerts, model routing)
  • Incident runbook + post-engagement playbook

Cross-cutting

Engineered into every phase — not bolted on

Security and governance show up in every phase, not as a separate workstream. Here's where each artifact lives.

Phase 1

Data classification & flow mapping

Phase 1–2

PII boundaries & data minimization design

Phase 2–3

Model logging, prompt logging, audit trail

Phase 2

Access controls & role mapping

Phase 3

Reviewer-in-the-loop policy

Phase 3–4

Change control & deployment gates

Phase 0

Sub-processor disclosure

Phase 4

Incident runbook & post-engagement playbook

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 tools we build with create their own attack surface — IP leakage, secret exposure, vulnerability injection. We govern the development environment with a four-tier framework before any code gets written.

Tier 1

Configuration

We enforce enterprise-tier settings on every AI assistant we deploy: data sharing and training disabled, sensitive-file exclusions configured at the project root, telemetry locked down. Default settings ship code logic to third-party servers — we don't accept the defaults.

Tier 2

Code Security

Automated guardrails (secret scanning, SAST tools like Snyk and SonarQube) and pre-commit hooks (GitLeaks-class) catch credentials and bad patterns before code is merged. AI is statistically good at syntax — and statistically prone to suggesting vulnerable patterns. We catch them at the gate.

Tier 3

Network

For high-compliance areas (healthcare PHI, financial NPI, regulated payments), we route AI traffic through private endpoints — Azure OpenAI, AWS Bedrock with VPC isolation, or your existing model gateway — to guarantee data sovereignty and avoid cross-tenant exposure.

Tier 4

Culture & Governance

Tools are half the answer. We train your engineering team on AI Skepticism — code review policies that require higher scrutiny on AI-generated code, ensuring human oversight stays the final gatekeeper. Adoption is the security control that lasts.

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 the honest answer.

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: Discovery

FusionLeap

Stakeholder interviews, process mapping, data flow & PII assessment, requirements doc, AI opportunity roadmap

Your team

Make 8–15 stakeholders available (1 hr each), provide read-only data access, share strategic context

Phase 2: 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: Hardening & Demo

FusionLeap

Error handling, observability, integration tests against production data, security validation, hand-off 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

Optional Fractional retainer, post-engagement check-in at week 4 and quarter 1

Your team

System ownership, on-call coverage, QBR cadence

Client effort estimate

Expect 6–10 hours/week of senior client time across the first 4 weeks, dropping to 3–5 hours/week thereafter. Total client effort: 60–100 hours over the engagement, concentrated in three roles: executive sponsor (2–4 hrs/wk for governance), data SME (3–5 hrs/wk for build feedback), and engineering team (5–8 hrs/wk during Phase 4 handoff).

Our Principles

  • We Start With Why. Every solution must tie to a business outcome we can name in dollars or hours.
  • We Show, Don't Tell. Working code every week. No deck-only weeks.
  • We Use Your Data. Real results, not sanitized examples. From Week 3 onward.
  • We Transfer Knowledge. No black boxes. Your team owns the codebase from day one and operates the system by Week 12.
  • We're Honest About Limits. We'll tell you when AI isn't the answer, when a Big-4 is the better fit, or when your internal team should own this.
  • We Optimize for Speed AND Quality. Fast because we're experienced, not sloppy.
USPTO Patent Pending · App # 63/952,353

How we hit these timelines

Our patent-pending Unified Dependency Graph mathematically links every line of code to the infrastructure it spawns and the cost it incurs. This is internal tooling — it accelerates our delivery by eliminating the guesswork that slows most engagements. Yours, not ours: code we ship to you doesn't depend on it.

This is FusionLeap platform tooling, not client-deliverable IP.

Ready to run an engagement like this?

We'll tell you which engagement fits — Sprint, Rapid AI Development, or Fractional — and what your first 90 days would look like.

Discuss Your Challenge