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Products with intelligence built in.

We build AI features that answer from your data, fit into your product, and hold up when real users start asking messy questions.

Scope your project
Production
Built beyond the demo stage
Grounded
Answers tied to your data
Measured
Quality tested with evals

Overview

The gap between a demo and a product is trust. We engineer for the product.

Useful AI is not a chatbot bolted onto a page. It has to understand the task, respect the workflow, answer from the right knowledge, and fail gracefully when it does not know enough.

We design and build AI systems around real product behavior: retrieval over your documents and data, model selection for the job, evals that measure quality, and guardrails that make the experience reliable enough to ship.

What we build.

AI systems for real workflows, not just impressive demos.

Knowledge copilots

Internal assistants that help teams search policies, docs, project history, support notes, or operational knowledge with cited answers.

Customer support AI

Support assistants that answer common questions, surface source material, draft replies, and hand off cleanly when a human is needed.

Document workflows

Systems that read, classify, summarize, extract fields, and route documents across finance, legal, operations, or admin workflows.

AI search

Search experiences that understand meaning, not just keywords, with retrieval tuned around your content and user intent.

Product copilots

AI features inside SaaS or internal tools that help users write, analyze, decide, and move through complex workflows faster.

Workflow automation

AI-assisted processes that turn messy inputs into structured actions, approvals, drafts, summaries, or next-step recommendations.

What we actually deliver.

The parts that make an AI feature useful, reliable, and safe enough to put in front of real users.

Grounded in your knowledge

Retrieval over your documents, database records, and product context, with citations where the user needs to trust the answer.

Measured, not vibes

Real evaluation suites that score quality, so we tune prompts and retrieval against evidence.

Safe in the wild

Guardrails, cost controls, monitoring, and fallback paths so the system behaves and you know when it needs attention.

Our process.

Four steps, one team, no hand-offs. You see progress every week and own every decision along the way.

01

Use-case shaping

We find where AI actually earns its place in your product, what success should look like, and where simpler software is the better choice.

02

Data & retrieval

We connect your documents, data, and product context so the model can answer from your knowledge instead of guessing.

03

Build & evaluate

We build the user experience, prompts, retrieval, and eval suites together, then tune against evidence instead of opinions.

04

Ship with guardrails

We launch with monitoring, cost controls, fallback behavior, and safety guardrails so the system can improve without becoming risky.

Our Stack

Models

Language modelsReasoning modelsLong-context modelsFast task models

Data & Context

RAGEmbeddingsHybrid searchKnowledge graphs

Vector DBs

QdrantPineconepgvector

AI Workflows

LangChainLangGraphTool callingFunction callingAgentsStructured outputsDocument extraction

Quality & safety

EvalsGuardrailsCost controlsMonitoringTracingExplainabilityInterpretabilityObservability

Common questions

Will it hallucinate?

No AI system can promise zero wrong answers, but we reduce risk by grounding responses in your data, showing citations where useful, testing quality with evals, and adding fallback behavior when confidence is low.

Which models do you use?

We choose the right model for the job based on quality, speed, cost, context length, and safety needs, then design the system so models can be swapped or upgraded as the landscape changes.

Can it connect to our existing docs or database?

Yes. We can connect to documents, help centers, internal wikis, databases, APIs, and product data, then shape retrieval around the questions users actually ask.

Do we need clean data before starting?

Not perfectly clean. We usually start by auditing the sources you already have, identifying gaps, and deciding what needs cleanup before it becomes part of the AI system.

How do you control cost?

We control cost with model selection, caching, retrieval limits, prompt design, usage caps, monitoring, and routing simpler tasks to cheaper models where quality allows.

Can this live inside our existing app?

Yes. We can add AI features inside an existing product, build a standalone internal tool, or create a new AI-native product from scratch.

Let's scope your AI product.

A few quick questions so we come to the first call already knowing your project. No long forms — leave your email and we'll reply within one business day.

You get
A tailored, no-obligation proposal
Response time
Within 1 business day
Cost
The conversation is free
Step 1 of 5
Your product

What are you looking to build?