// SERVICE · AI PRODUCT FEATURES

EmbedAI.Withoutthedemo→prodcliff.

WHO THIS IS FOR

For product teams adding AI to existing software (copilots, search, summarization, classification) and tired of the gap between a flashy demo and a reliable shipped feature.

We embed LLMs, agents, and AI features into your product or internal tools, with the eval frameworks, observability, and reliability scaffolding most teams skip. Designed to handle real users, real data, and the long tail of edge cases your demo never tested.

Book a scoping call →See how we work

// USE CASES

What we actually build.

PROBLEM

Document analysis copilot

Users upload contracts, get a 'summary' that hallucinates clauses 18% of the time.

SOLUTION

RAG with strict citation grounding, eval set of 200 contracts, refusal path for low-confidence answers.

STACK

ClaudepgvectorTypeScript

TIMELINE

5–6 weeks

PROBLEM

In-product copilot

Users want a chat interface to your product, but it has to actually work: read state, take actions, refuse cleanly.

SOLUTION

Tool-calling agent over your existing API + audit log + evals on action correctness.

STACK

OpenAITypeScriptyour API

TIMELINE

6–8 weeks

PROBLEM

Semantic / RAG search

Keyword search misses the actual intent. Users find nothing, then leave.

SOLUTION

Hybrid retrieval (BM25 + embeddings) + re-ranker + relevance evals against your real query log.

STACK

CoherepgvectorOpenAI

TIMELINE

4–5 weeks

PROBLEM

Natural-language reporting

Users want 'show me MRR by cohort last quarter' without writing SQL.

SOLUTION

Schema-aware text-to-SQL with strict allowlist + result preview + eval on 300 historical questions.

STACK

ClaudeBigQueryNext.js

TIMELINE

5–7 weeks

PROBLEM

Smart classification

You need to label 50K user-submitted items into one of 18 categories.

SOLUTION

LLM classifier with structured output + eval harness + active-learning loop for new categories.

STACK

OpenAIPythonFastAPI

TIMELINE

3–4 weeks

// SAMPLE DELIVERABLE

What you walk away with.

product-features-handoff/ — 14 files · 2.4 MBmain

📁 product-features-handoff/

📄 README.md

📄 architecture.md

📁 evals/

groundtruth.jsonl

dashboard.html

regression.yml

📁 src/

feature.ts

retrieval.ts

prompts.yaml

📁 deploy/

terraform/

monitoring.json

📄 runbook.md

🎬 walkthrough.loom

# product-features-handoff

## Stack: OpenAI · Claude · TypeScript · pgvector

## What's in here

Complete handoff for the AI feature integration. Models are

version-pinned; prompts are committed to git.

## Features included

- rag-copilot — citation-grounded document Q&A

- semantic-search — hybrid BM25 + embedding retrieval

- nl-reporting — text-to-SQL with allowlist

## Running evals

$ npx ts-node evals/regression.ts --suite groundtruth.jsonl

## Observability

Traces exported to dashboard.html. Prompt drift alerts

configured in monitoring.json. See runbook.md for rollback.

## Loom walkthrough

21-minute recorded handoff at walkthrough.loom

// FAQ

Common questions for this kind of work.

How do you decide between fine-tuning, prompting, and RAG?

Default to prompting + RAG. Fine-tune only if you have ≥10K labeled examples, latency/cost matters more than flexibility, and we've hit a quality ceiling without it. Most projects never need to.

How do you handle prompt and model drift?

We pin model versions, snapshot prompts in git, and run the eval harness on every change. When you upgrade models we re-run the suite and tell you exactly what regressed.

Will you sign a BAA / DPA / sub-processor list?

Yes. We can also run entirely inside your cloud and use your model keys, so no customer data crosses our infrastructure.