Document & data extraction
PDFs, photos, invoices and receipts turned into structured data your systems can use — with validation, not hope.
AI product development
From AI prototype to a product people pay for.
We design, build and run AI products end to end — the model integration, the guardrails, the app around it and the operations after launch. Built with AI, reviewed and owned by senior engineers.
Your productweb · iOS · Android · admin Flutter · React
API & dataauth · billing · storage · queues Go · Node.js · PHP
AI layerwhere most AI projects fail
- output contracts
- injection defenses
- evals
- cost ceilings
- retries
AWS Bedrockmanaged models Self-hostedLlama · Qwen · Mistral
What we build
AI that does a real job inside your product.
Search & answers over your data
Natural-language search and grounded answers over your catalog, docs or knowledge base, with sources users can check.
Assistants & copilots
In-app assistants that know your product and your user’s context — scoped, safe and measured against real tasks.
Agents & workflow automation
Multi-step agents that call your tools and APIs, with human approval where it matters and a full audit trail.
Why it holds up
A demo takes a weekend. Production takes engineering.
Models are non-deterministic, inputs are hostile and every call costs money. We design for all three from day one — the same way we run ParsePrime.
Strict output contracts
JSON against a fixed schema. Anything that doesn’t parse is rejected, never shown.
Prompt-injection defenses
User content is data, never instructions. Suspicious input is flagged, not obeyed.
Evals before every release
A test set of real cases, rerun on each prompt or model change, so quality is measured, not felt.
Predictable cost
Token budgets per tier, caching and the smallest model that does the job.
Safe outputs
Sanitized exports, signed expiring links, automatic retries on transient errors.
Monitoring in production
Latency, cost and failure rates tracked per feature, with a rollback ready.
How we work
Prove it small, then make it solid.
AI drafts the code inside our internal agent harness. Senior engineers set the architecture, review every change and own every release.
- 01 · Discover
Pick the job AI should do
Goals, data, constraints and what “good” looks like — written down as test cases.
- 02 · Prototype
Test on your real data
Compare models and prompts against the test set before building around them.
- 03 · Build
Ship the MVP
App, API and AI layer, with weekly demos you can touch.
- 04 · Harden
Guardrails & evals
Injection tests, edge cases and cost limits — reviewed by a senior engineer.
- 05 · Operate
Launch and improve
Monitoring, model upgrades and new features driven by real usage.
Where your AI runs
Managed or self-hosted — your data decides.
AWS Bedrock
Managed models through one API, paid per token. Data stays in your AWS account and region. Best for a fast start and variable load.
Self-hosted open models
Llama, Qwen or Mistral via Ollama or vLLM on your own GPUs. Data never leaves your infrastructure. Best for sensitive data and steady high volume.
Our own AI product ParsePrime: AI table extraction, live and paid.
Upload a PDF or a photo, get clean CSV tables back. We built and run every layer — which is why we can say how AI behaves in production, not just in a demo.
- A vision LLM reads multi-row headers, merged cells and receipts without grid lines
- Tables that span pages are stitched back together
- Accounts, credits, Stripe checkout and a REST API for automation
- Flutter web app in 11 languages, PHP backend and admin panel
Ways to work together
Start where you are.
AI MVP
A new AI product, from idea to first paying users — the smallest scope that proves the idea.
AI in your existing product
One AI feature added to the app you already have: extraction, search, an assistant or an agent.
See AI integrations →Ongoing AI team
A senior-led team that builds, operates and improves your AI features month after month.
AI agents and team training
Agents built on Claude for your departments — tenders, technical, operations — plus training so your team uses them, improves them and builds new ones on its own.
See an example: a construction company → FAQ
AI product development, answered.
How is AI product development different from regular software development?
The model is non-deterministic, so quality has to be measured with evals, outputs have to be validated and cost has to be designed in. The app around it is classic engineering — and still most of the work.
Which AI model should we use?
We decide with data: candidate models run against your test set, and we pick on accuracy, latency, cost and where your data may go — managed on AWS Bedrock or open-source models self-hosted.
Can our data stay private?
Yes. With Bedrock it stays in your AWS account and region; with self-hosted models it never leaves your infrastructure.
You build with AI — who checks the code?
Senior engineers. AI drafts, people review every change, test on real devices and own the release.
Can you add AI to an app we already have?
Yes — it is the most common starting point. We work in Flutter, React, Go, Node.js and PHP codebases.
Who owns the code, the prompts and the agents?
You do. Everything we build for your project — code, prompts, skills and agents, evaluation sets and documentation — belongs to you and stays with you after the engagement ends.
Got an AI idea? Let’s test it on your data.
Tell us the job you want AI to do. We come back with a proposed scope, timeline and cost.