Can you work in our existing stack?

Yes. We prefer shipping in your repo, cloud, and CI. Greenfield only when it clearly reduces risk or time-to-value.

How do you handle model choice and vendor lock-in?

We design for provider abstraction where it matters, pick models based on eval results and cost, and document tradeoffs so you can change course later.

What does a first engagement typically include?

Discovery, architecture for a scoped slice, production implementation with evals and observability, and a clear roadmap for what comes next.

How do you measure quality?

Task-specific evals, production traces, latency and cost budgets, and user outcome metrics — not vibe checks on chat transcripts.

What if we only need architecture or a review?

We can run a focused audit or architecture engagement. Most teams then continue into a build sprint once the path is clear.

AI Product Engineering

Ship production AI, not prototypes.

For B2B SaaS founders who need agents, RAG, and LLM features in production — with evals, observability, and a shipping cadence that matches your product org.

Book a strategy call

See how we work

AI copilot interface showing production assistant workflows

Production ready

Evals · Observability · Shipping

Problems we solve

Where AI initiatives stall.

Common failure modes we see before a system is ready for customers.

Stuck in prototype

Demos impress stakeholders, but the path to reliability, latency, and cost control is undefined.

No internal AI expertise

Your product team can ship features — but not LLM architecture, eval harnesses, or retrieval quality.

Limited engineering bandwidth

Core roadmap is full. AI work gets weekends and spikes, never a sustained delivery system.

Need to launch quickly

Competitors are shipping copilots. You need a scoped first slice live in weeks, not a six-month research project.

What we deliver

Systems you can put in front of customers.

Scoped product slices with architecture, evals, and observability from day one.

AI MVP Development

First production slice — scoped, measurable, and integrated into your existing product.

AI Copilots

In-product assistants grounded in your domain data, workflows, and permissions model.

RAG Systems

Retrieval pipelines with chunking strategy, ranking, citations, and quality gates.

AI Agents

Tool-using agents with guardrails, state, and human-in-the-loop where it matters.

LLM Integrations

Model routing, prompt systems, and provider abstraction built for cost and reliability.

Workflow Automation

AI-backed ops flows that reduce manual review without sacrificing auditability.

Process

From discovery to continuous improvement.

A clear path from problem framing to production — without theater.

01 / 05
Discover
Map use cases, constraints, data readiness, and success metrics with your product and eng leads.
02 / 05
Architecture
Define model strategy, retrieval, evals, observability, and the smallest shippable slice.
03 / 05
Build
Implement in your stack with code review, tests, and instrumentation — not a black-box demo.
04 / 05
Deploy
Ship behind flags, monitor quality and cost, and validate against real user traffic.
05 / 05
Optimize
Tighten evals, reduce latency and spend, and expand scope once the foundation holds.

Technology

Pragmatic stack choices.

We work in your environment. These are the systems we ship with most often.

LLMs

OpenAI
Anthropic
Gemini
Open-source

Frameworks

LangChain
LlamaIndex
Vercel AI SDK
Custom

Infrastructure

AWS
GCP
Vercel
Docker

Databases

Postgres
pgvector
Pinecone
Redis

Deployment

CI/CD
Feature flags
Observability
Evals

Sample engagement

From demo-ware to a customer-facing AI system.

B2B SaaS · Product & engineering engagement

AI copilot interface embedded in a product workspace

Series B workflow SaaSIn-product AI · Production readiness

Audit

Reliability map

Latency, hallucination, and access gaps

Design

Eval + telemetry plan

Harness, metrics, and rollback path

Sprint

Gated production slice

Feature-flagged in-product copilot

Problem

An internal AI prototype impressed stakeholders, but failed latency, hallucination, and access-control checks required for paying customers.

What we install

Rebuild retrieval with domain chunking, citations, and permission-aware filters
Add an eval harness and production telemetry before expanding feature scope
Ship a gated copilot to a customer cohort with a clear rollback path

Discuss a similar engagement

Illustrative engagement shape — scoped to your product and stack on the call.

Why Peergrowth

How we compare for AI product work.

Qualitative tradeoffs — so you can pick the model that fits your stage.

Category	Peergrowth	Hiring internally	Freelancers	Traditional agencies
Time to first ship	Scoped slice in weeks	Months to hire + ramp	Depends on availability	Long discovery cycles
Production readiness	Evals + observability included	Must build the muscle	Often demo-focused	Rarely owns reliability
Stack fit	Works in your codebase	Full control	Variable depth	Often parallel prototypes
Knowledge transfer	Documented + handoff ready	Stays on payroll	Person-dependent	Slide decks, limited code
Commercial model	Outcome-aligned engagement	Ongoing headcount cost	Hourly / unpredictable	High fixed retainers

Scroll horizontally to compare all options.

FAQ

Technical and commercial questions.

Straight answers. Anything else is better on a call.

No. We embed alongside your product and eng leads — architecture, implementation, and quality systems — then hand off cleanly.

Next step

Ready to ship production AI?

Book a strategy call. We'll map your use case, constraints, and the fastest path to a reliable first slice.