Implementation Consulting · Enterprise

Generative AI Implementation Consulting

Implement generative AI in the enterprise the way production software is implemented — in your cloud, gated by evals, handed over to your team.

Service
Implementation
Industry
Enterprise
Updated
2026-08-25
Engagement
4 wks
The short answer

Generative AI implementation consulting takes a bounded enterprise workflow from discovery to production: environments in your cloud, a shadow-mode pilot, evaluation with golden sets and CI gates, and handover of weights, datasets, eval suites, and runbooks — typically in four weeks. ReinforcedX quotes a platform subscription plus a fixed-scope fee before week one and includes 30 days on-call after handover.

The premise

Standard implementation is four weeks: discovery, environments, shadow-mode, handover.

Engagement
4 wks
discovery to handover
Week 3
shadow-mode on real traffic
0 markup
you pay the model provider
The path
01Discover
02Design
03Build
04Validate
05Deploy & Enable

Why teams pick this engagement

Implementation × Enterprise

The four-week shape

Week one discovery, week two environments, week three shadow-mode, week four handover. That is the standard for a well-bounded workflow.

Your stack, not a sidecar

Connectors, identity, traces, and models live where your other systems live. We integrate; we do not replace your cloud.

Zero-retention default

Client data is not used to train shared models. Provider settings default to zero-retention. Work never copies the corpus onto our infrastructure.

CI gates, not a launch meeting

Golden sets and rubric judges block promotion. If the suite is red, the system does not take traffic — regardless of the calendar.

Handover is the product

Your engineers leave with runbooks, eval triage, and 30 days on-call. One process owner plus one engineer during the build.

Quoted before week one

Platform subscription plus a fixed-scope implementation fee, in writing. Inference is billed by you to the provider; no token markup.

Key takeaways

  • 01

    Standard implementation is four weeks: discovery, environments, shadow-mode, handover.

  • 02

    Work runs in the client’s cloud; data is not used to train shared models; zero-retention is the default.

  • 03

    Evaluation uses golden sets, rubric judges, and CI gates before live traffic.

  • 04

    You own weights, datasets, eval suites, and runbooks; 30 days on-call follow handover.

  • 05

    Inference is billed by the client to the provider — Anthropic, OpenAI, Google, Mistral, or a fine-tune — with no token markup.

What the engagement covers

01

Discovery & Environment Plan

Week one bounds the workflow, metric, sources, and risks. Week two stands up environments and connectors on your identity and observability stack.

02

System Build & Tooling

Retrieval, tools, permissions, and traces implemented against your systems. Model-agnostic routing so a provider change is an eval-gated config, not a rewrite.

03

Shadow-Mode Pilot

Week three runs the system on real traffic without taking the action path, scored against the golden set. Failures become labels, not anecdotes.

04

Validation & Promotion Gates

Rubric judges, regression in CI, permission tests, and fallback. Promotion is a green suite, not a steering-committee vote.

05

Handover, Enablement, On-Call

Runbooks and working sessions with your engineer. Thirty days on-call after week four. The weekly 45-minute review continues through that window.

How we work

  1. 01

    Discover

    Workflow, success metric, sources, and risks. Unbounded work is not scheduled as “implementation.”

  2. 02

    Design

    Architecture, permissions, eval plan, and model route before environments go live.

  3. 03

    Build

    Week-two environments and connectors; traces on from the first call.

  4. 04

    Validate

    Week-three shadow-mode against golden sets, rubric judges, and CI gates.

  5. 05

    Deploy & Enable

    Week-four handover of artifacts, then 30 days on-call.

Take the playbook with you

The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.

Flagship resource · PDF · 12 pages

The Four-Week Generative AI Implementation Plan

Day-by-day plan for discovery, environments, shadow-mode, and handover, including the weekly 45-minute review agenda.

Get the plan ·
DOCX · 10 pages

Generative AI Implementation Runbook Template

Incident, fallback, eval-triage, and rollback sections — the structure we hand over in week four.

Get the template ·
XLSX worksheet

Shadow-Mode Exit Criteria Worksheet

The gates that decide when shadow traffic can become production traffic: golden-set pass, permission checks, trace coverage.

Get the worksheet ·

Frequently asked questions

How do you implement generative AI in the enterprise?

Bound one workflow, stand up environments in your cloud, run shadow-mode on real traffic, gate promotion with golden sets and CI, then hand over weights, datasets, eval suites, and runbooks. That is four weeks when the workflow is well bounded. Financial-services governed pilots typically take 8–12 weeks; healthcare 10–14; ecommerce 6–10. Details of the calendar are on /faq.

What is generative AI implementation consulting?

It is the build: architecture, integration, evaluation, and handover inside your perimeter — not a workshop and not staff augmentation. ReinforcedX quotes a platform subscription plus a fixed-scope fee before week one. You pay the model provider; there is no token markup. One process owner plus one engineer run the work. Book /demo to put a workflow on that calendar.

How long does generative AI implementation take?

Four weeks is the standard for a well-bounded workflow: discovery, environments, shadow-mode, handover. A demo can exist in days; production is permissions, traces, fallback, and a green eval suite. Heavier regulated reviews take longer because the governance layer is heavier. Thirty days of on-call follow handover.

What happens in each of the four weeks?

Week one: workflow, success metric, sources, risks. Week two: environments, connectors, traces. Week three: shadow-mode on real traffic against the golden set. Week four: permissions, runbook, handover. The weekly 45-minute review is the only standing meeting. If week one cannot bound the work, we stop rather than invent a metric.

Will implementation lock us into one model vendor?

No. The stack is model-agnostic: Anthropic, OpenAI, Google, Mistral, and client fine-tunes. Pin a version, route easy cases to a smaller model, fall back when a provider degrades. Switching models is an eval-gated config change when traces and prompts were built that way from day one. You pay the provider directly.

Where does the implementation run?

In the client’s cloud, against your identity provider and data stores. We do not copy the corpus onto ReinforcedX infrastructure. Zero-retention provider settings are the default. Client data is not used to train shared models. Fine-tunes stay in your tenancy. That perimeter is the same rule stated across /faq and quoted before week one.

How do you know the implementation is ready for production?

A green golden set, rubric judges in CI, reconstructable traces, permissions that match live entitlements, and a runbook your engineer has used in rehearsal. Shadow-mode in week three is the dress rehearsal. If those gates are red, we do not cut over because the calendar says week four. Evaluation patterns also live under /ai-systems.

Can our team implement this themselves from your guides?

Yes. /how-to exists because plenty of teams would rather build it. If you would not, the same work runs as the fixed-scope engagement: four weeks to production, and you own everything that ships. Either path uses golden sets, rubric judges, and CI gates. We will not pretend a workshop is an implementation.

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