LLM features in your app
Natural-language search, summaries, extraction and classification, added to the screens and APIs your users already use.
AI integrations & agents
AI inside the product you already have.
We wire LLMs and agents into your existing app, backend and workflows — on AWS Bedrock or on open-source models you host yourself. Your code stays your code; the AI layer gets the guardrails, evals and cost limits production needs.
Your stack, plus an AI layerin the codebase you have
- Your appFlutter · React · web
- Your backendGo · Node.js · PHP
- AI layerprompts · tools · evals · guardrails
- ModelsAWS Bedrock or Llama · Qwen · Mistral
What we integrate
From one AI feature to agents that run a workflow.
Agents that use your tools
Agents that call your APIs and databases through defined tools, ask a person before risky steps and log every action.
Agent harnesses
The runtime around the model: tools, permissions, memory, retries, cost limits and an audit trail. We build it for your stack; running our own internal harness taught us what it needs.
Private, self-hosted AI
Open-source models (Llama, Qwen, Mistral) served with Ollama or vLLM on your infrastructure, for data that must not leave it.
Why it holds up
Calling an API takes a day. Running it in production is the job.
An integration touches real users, real data and a real bill. These are the parts we build in from the start.
One interface, swappable models
The AI sits behind one interface in your code, so the model or provider can change without touching the product — as ParsePrime moved from a local Qwen model to Gemini.
Tools with permissions
Agents reach only the actions you allow. Anything that writes, sends or pays waits for a person to approve it.
Evals on your real cases
A test set built from your data, rerun on every prompt or model change, so quality is measured, not felt.
Injection-safe by design
Documents, emails and user input are treated as data, never as instructions.
Cost and latency limits
Token budgets, caching, timeouts and retries per feature, so the bill stays predictable.
Logs and an audit trail
Every model call and agent action is recorded, so you can explain what the AI did and why.
How we work
Start with one workflow. Make it solid.
AI drafts the code inside our internal agent harness. Senior engineers design the integration, review every change and own every release.
- 01 · Audit
Map the workflow
Where AI helps, which data and systems it needs, and what could go wrong.
- 02 · Prototype
Test on your data
Models and providers compared against real cases before anything is built around them.
- 03 · Integrate
Wire it into your stack
APIs, tools and UI in your codebase, with weekly demos you can touch.
- 04 · Harden
Guardrails & evals
Permissions, injection tests and cost limits, reviewed by a senior engineer.
- 05 · Operate
Run and improve
Monitoring, model upgrades and new tools, driven by real usage.
Where your AI runs
Managed or self-hosted — your data decides.
| AWS Bedrock | Self-hosted open models | |
|---|---|---|
| Best for | Fast start, top-tier model quality, variable load | Sensitive data, steady high volume, full control |
| Models | Managed foundation models through one API | Llama, Qwen, Mistral via Ollama or vLLM |
| Data | Stays in your AWS account and region | Never leaves your infrastructure |
| Cost | Pay per token | Pay for GPUs; cheaper at steady scale |
| Typical setup | A Lambda function or ECS service calling Bedrock | Ollama to prototype, vLLM for production throughput |
Proof
AI we have shipped and run.
A vision LLM in production
ParsePrime, our own product: documents in, CSV tables out, with strict schemas, injection defenses and per-tier cost budgets.
Read the ParsePrime story →AI agents in a construction company
Agents on Claude for the tenders, civil works and hydro departments, plus training so the team builds its own.
Read the case →Natural-language search in a mobile app
In the WeReload app, users type what they want and Groq returns matching results in under a second. We published the Dart SDK we built for it.
groq on pub.dev → FAQ
AI integrations, answered.
Can you add AI to our app without a rewrite?
Yes. The AI layer goes next to your existing code, usually as a service and a few endpoints. We work in Flutter, React, Go, Node.js and PHP codebases.
AWS Bedrock or a self-hosted model — how do we choose?
By where your data may go, how much traffic you expect and the quality the task needs. We run candidate models against your own cases and decide with the results.
What is an agent harness?
The software around a model that turns it into an agent: the tools it may call, permissions, memory, retries, limits and logs. Without it, an agent is a demo.
Will our data be used to train models?
Not with the setups we build. On AWS Bedrock, prompts and outputs are not used to train the models and stay in your AWS account and region; with self-hosted models nothing leaves your infrastructure.
Can an agent act on its own?
Only as far as you allow. Read-only steps can run automatically; anything that writes, sends or pays waits for a person to approve it.
Who owns the code, the prompts and the agents?
You do. Code, prompts, tools, evaluation sets and documentation belong to you and stay with you after the engagement ends.
Tell us the job. We’ll design the integration.
Describe the workflow or the feature. We come back with an approach, a timeline and a cost.