The Forward Deployment Lifecycle

How we deploy —
sprint by sprint.

Every forward deployment follows the same proven lifecycle — 2-week sprints, clear metrics, embedded ownership from kickoff to outcomes.

The Sprint Model

From fit to full deployment in 6 sprints.

Two-week cycles. Measurable progress at each gate. Nothing ships without meeting the quality bar.

Sprint 0Week 1

Product Fit + Kickoff

Product selected, success metrics defined, stakeholders aligned, environment access granted. Your FD Engineer and FD PM are introduced and embedded.

Sprint 1Week 2–3

Data & Configuration

Data sources connected, RAG pipeline built, security policies applied, initial product configuration complete. Every integration tested before pilot.

Sprint 2Week 4–5

Internal Pilot

Champion team goes live. Feedback collected. RAG quality scored — retrieval precision, faithfulness, latency. Fixes shipped same sprint, not queued.

Sprint 3Week 6–7

Agent Deployment

Agentic workflows built, tested, and validated against task completion benchmarks. Edge cases handled. Failure modes enumerated before broad rollout.

Sprint 4Week 8–9

Org-Wide Rollout

Phased rollout with structured training. Change management program active. Adoption dashboards live. Champion network activated across departments.

Sprint 5+Ongoing

Measure & Optimize

Monthly sprints: outcome reviews, model tuning, new use case expansion, product upgrades. FD team stays embedded. You never optimize alone.

RAG Standards

How we know the AI is working right.

Every deployment is measured against the same four quality gates. No gut feel — just numbers.

Retrieval Precision

>85%

Of retrieved chunks are relevant to the query. Measured per sprint using an internal eval suite against your actual documents.

Answer Faithfulness

>90%

Of answers are grounded in retrieved context with no hallucination. We run automated faithfulness scoring on every deployment.

Latency

<200ms

Retrieval latency target, <3s end-to-end response. Monitored continuously; optimized each sprint if thresholds are missed.

Coverage

>95%

Of your indexed documents are queryable. We audit gaps and add missing sources each sprint — no blind spots.

Agent Deployment Standards

How we build and validate agents.

Every agent meets three standards before going to production — no exceptions.

Task Completion Rate

>90%

Every agent is benchmarked on a defined task set before going live. We do not ship agents that can't pass their production workload benchmark.

Failure Mode Coverage

100%

We enumerate failure modes before deployment and build fallback paths for each. Agents are never deployed without a human escalation path.

Observability

Every action

Every agent action is logged. We review agent traces each sprint and flag anomalies before users notice them.

What Comes Next

Forward deployment doesn't stop at launch.

Every engagement is designed to compound — more value each sprint, not diminishing returns.

New Use Cases

Each sprint review surfaces new opportunities. We scope and deploy them in subsequent sprints — expanding the value of your existing AI product investment without adding cost.

New Products

As your org matures, we layer in additional products. A team already running Cursor might add Glean for knowledge search. We manage the full portfolio.

Capability Upgrades

AI products evolve fast. We track every product update and assess which new features should be activated in your deployment — you never fall behind.

How We Work

The forward deployment engagement model.

Clear deliverables, transparent timelines, no surprises.

01

Product Fit Assessment

Free session where we analyze your team, stack, and goals — and tell you exactly which AI product to deploy and why. No vague audits, just a clear product recommendation.

02

Scoped Deployment Plan

A sprint-by-sprint plan: which product, which configuration, what RAG pipeline, which teams roll out first, and what success looks like at each milestone.

03

Embedded Deployment

Our FD Engineer and FD PM embed at your org and run the full deployment lifecycle — from data ingestion to org-wide rollout. 60–90 days to full production.

04

Ongoing Optimization

Monthly FD subscription: sprint reviews, outcome measurement, model tuning, and expansion. Cancel anytime — we earn your renewal by hitting your metrics.

Ready to see your
deployment roadmap?

Book a free Product Fit Assessment. We'll tell you which AI product fits your team and what full deployment looks like — before you commit to anything.