LLM INTEGRATION SPRINT — AI SPRINT

Jake McMahon — ProductQuant

B2B SaaS · Product · Growth

LLM Integration Sprint

GPT-4, Claude, Gemini, or open-source LLMs wired into your product with prompt engineering, guardrails, and evals.

Book a Scoped Call → See Deliverables ↓

Free diagnostic · Blueprint sprint with money-back guarantee · Full handoff

WHAT YOU HAVE AT THE END

LLM Feature Wired into your product, tested and monitored

Prompt Architecture Documented prompt stack — versioned and repeatable

Safety Guardrails Content filters, rate limits, cost controls built in

Eval Framework Test suite to measure accuracy and catch regressions

Handoff Package Source code, docs, integration guide — fully yours

Fixed price · Everything stays with you

LLM Integration Sprint — in plain terms.

Here’s what this looks like in practice:

CHAT FEATURE

You ship a chat assistant inside your product

Users ask questions in plain language. The LLM answers based on your content — with guardrails that prevent it from going off-topic or generating harmful output.

SUMMARISATION

Long documents → structured summaries users can act on

Your users paste in a contract, report, or article. The system returns a structured summary in the format you define — highlights, action items, or key clauses.

CLASSIFICATION

Incoming data routed automatically by content type

Support tickets, form submissions, or documents arrive and get classified and tagged instantly — without a human reading each one.

CONTENT GENERATION

Drafts generated from your templates and data

Your team defines the template and data inputs. The LLM generates first drafts — proposals, emails, product descriptions — ready for human review and approval.

ENTRY POINT

Free Diag.

Every engagement starts with a free 45-minute diagnostic. We map your situation and tell you whether this sprint is the right fit before you spend a dollar.

GUARANTEE

Money-Back

Blueprint sprint has a money-back guarantee. If the agreed deliverable isn’t met, you pay nothing. No conditions, no argument.

OWNERSHIP

Full Handoff

Everything built during the engagement — code, models, documentation — is yours. No lock-in, no ongoing dependency.

THE PROBLEM

API calls aren't a product

“"We connected the API in a day. But outputs are inconsistent and we've had two incidents where it said something wrong to a customer."”

VP PRODUCT

No eval framework

“"We don't know if our prompts are getting better or worse. Every update is a guess."”

ENGINEERING LEAD

Cost unpredictability

“"We launched the feature and the API bill tripled in two weeks. We had to throttle it and customers noticed."”

CTO

Guardrails as afterthought

“"Legal found out we'd shipped without content moderation. We had to pull the feature."”

PRODUCT MANAGER

WHAT THE BLUEPRINT SPRINT UNCOVERS

The gap between where you are and where you need to be.

Prompt engineering is more than writing prompts

Prompt architecture — version control, structured testing, regression detection — is what separates a reliable AI feature from a fragile one.

Guardrails prevent incidents, not embarrassment

A content filter isn't about brand safety. It's about legal liability, user trust, and not being the company in the breach report.

Cost controls belong in the architecture

If cost controls are retrofit after launch, they're already too late. The sprint includes rate limiting and cost monitoring from the first build.

Evals are the only way to improve prompts

Without a test suite, every prompt change is a production experiment. The eval framework lets you measure whether a change is better — before users see it.

WHY THIS IS DIFFERENT

An LLM wired to an API endpoint isn't a product. It's a liability.

Most teams treat LLM integration as an engineering task: call the API, parse the response, display it. The feature ships. Then the incidents start — hallucinations, off-topic outputs, unexpected costs, and a support ticket nobody knows how to explain.

Every integration we build includes a prompt architecture that's versioned and tested, guardrails that prevent the feature from saying anything it shouldn't, cost controls so the bill doesn't surprise you, and an eval framework your team uses to measure accuracy before shipping any prompt change.

THE METHODOLOGY

The AI Build System

Four phases. Every AI engagement, every time.

PHASE 1

Ingest

Map and clean your data sources. Define accuracy targets and query patterns before writing a line of code.

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PHASE 2

Build

Train, fine-tune, and test the model on your corpus. Iterate until the target accuracy is hit.

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PHASE 3

Deploy

Ship to your environment — cloud or on-prem. Integrate with your product or internal tools.

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PHASE 4

Monitor

Live dashboard tracks performance from day one. You see what's working and what needs attention.

After handoff: your team updates data, the system retrains — no ongoing dependency on ProductQuant.

WHAT YOU GET

Everything your team needs to launch and maintain the system.

FULL ENGAGEMENT

Complete LLM Integration Sprint

A production-ready LLM feature inside your product — with prompt architecture, safety guardrails, cost controls, and a repeatable eval framework.

Production-deployed LLM feature inside your product
Full prompt architecture with version control and CI/CD integration
Safety and moderation layer with logging and incident response runbook
Cost monitoring dashboard with per-user and per-feature tracking
Eval framework with automated regression testing
Documentation and source code — full ownership, no lock-in

FIT CHECK

Is this the right sprint for your team?

GOOD FIT

Product teams who want to ship an AI-powered feature (chat,

The situation

Product teams who want to ship an AI-powered feature (chat, summarisation, generation, classification) without building the infrastructure themselves

What changes

Production-deployed LLM feature inside your product
Full prompt architecture with version control and CI/CD integration
Safety and moderation layer with logging and incident response runbook

You have a production-ready llm feature inside your product — with prompt architecture, safety guardrails, cost controls, and a r.

Jake McMahon — ProductQuant

Jake McMahon

B2B SaaS · Product & Growth · Behavioural Psychology & Big Data (Master’s)

I work with B2B SaaS product and operations teams to build and deploy the systems they need — without consuming their engineering capacity or waiting 18 months for the roadmap.

Every engagement starts with a free diagnostic and a scoped blueprint sprint with a money-back guarantee. If the sprint doesn’t hit the agreed target, it costs you nothing.

What does my team need to provide?

Data access, a point of contact, and a clear picture of the outcome you need. No engineering time required during the build phase.

Teams Jake has worked with

PRICING

Start with a free diagnostic. Commit when you’re confident.

STEP 1

Free Diagnostic

Free

45-minute scoped call

We map your current situation and data
We tell you whether this sprint fits your problem
You get a written summary of what we’d build
No commitment required to book

Book Diagnostic →

START HERE

STEP 2

LLM Blueprint Sprint

$2,500–$3,500

Fixed scope · 2 weeks · Money-back guarantee

Audit of your use case, target user queries, and acceptable output formats
Documented prompt architecture: system prompt, user turn design, output schema
Tested safety guardrails and content filters for your specific use case
Cost control setup: rate limiting, token budgets, monitoring dashboard
Eval framework: 50-query test suite with pass/fail criteria
Integration handoff: API wrapper code, environment setup guide

Start Sprint →

STEP 3 (OPTIONAL)

Full Engagement

$15K–$40K

Scope-dependent · Full production build

Production-deployed LLM feature inside your product
Full prompt architecture with version control and CI/CD integration
Safety and moderation layer with logging and incident response runbook
Cost monitoring dashboard with per-user and per-feature tracking
Eval framework with automated regression testing
Documentation and source code — full ownership, no lock-in

Discuss Scope →

If the blueprint sprint doesn't deliver a tested LLM integration with a documented eval framework and zero guardrail bypasses in testing, the sprint is free.

Questions.

Or book a call →

Which LLMs can you integrate? +

GPT-4o, Claude 3.5/3.7, Gemini 1.5/2.0, Mistral, and open-source models like Llama 3. The choice depends on your latency requirements, cost budget, and whether data must stay on-premise.

How do you handle hallucinations? +

Through a combination of prompt architecture (constraining the LLM's scope), retrieval augmentation (grounding answers in your data), output validation (structured schemas), and the eval framework (catching hallucinations in testing before they reach users).

What does the eval framework actually look like? +

A test suite of representative queries with expected outputs and pass/fail criteria. Run it before any prompt change. We build it during the sprint and hand it over — your team runs it independently.

How long does the full integration take? +

Blueprint sprint: 2 weeks. Full production integration: 4–8 weeks depending on feature complexity, data sources, and your team's integration bandwidth.

Do you manage the prompts after launch? +

No. Full handoff is the goal. We give you the prompt architecture, the eval framework, and the documentation to manage it in-house. If you want ongoing optimisation, that's the Growth OS.