AI Development
AI that works in production, not in a demo
We build AI-powered features into real products: document processing, intelligent workflows, natural language interfaces, and automation that scales.
Capabilities
What we build
LLM-powered document processing
Extract, classify, and transform unstructured documents into structured data your systems can use.
Natural language interfaces for business data
Let your team query databases, generate reports, and navigate complex data using plain English.
Intelligent workflow automation
Automate multi-step processes that require judgment, context, and decision-making beyond simple rules.
AI-assisted decision tools
Surface insights, flag anomalies, and recommend actions based on your business data and domain rules.
Custom AI agents for specific business tasks
Purpose-built agents that handle defined workflows end-to-end with human oversight where it matters.
Technology
Our AI stack
We pick the right model and tooling for each use case. No vendor lock-in, no hype-driven decisions.
Process
How we approach AI projects
01
Define the business problem
We start with the outcome you need, not the AI technique. Most AI projects fail because they start with the technology instead of the problem.
02
Prototype with real data in 2 weeks
We build a working prototype using your actual data so you can evaluate results before committing to a full build.
03
Build for production
Error handling, fallbacks, monitoring, cost controls, and graceful degradation. AI that works 95% of the time is not production-ready.
04
Iterate based on actual usage
We monitor real interactions, refine prompts, adjust models, and improve accuracy based on what users actually do.
Honest take
AI vs hype
We'll tell you when AI is the wrong solution. Sometimes a well-designed form or a simple rule engine does more than an LLM. We evaluate every use case honestly and recommend AI only when it delivers measurable value over simpler alternatives.
FAQ
Questions teams ask before starting an AI project
Do I need my own AI team?
No. We handle the full AI stack from architecture to deployment. If you have data engineers or ML staff, we integrate with them. If you don't, we cover everything.
What data do you need access to?
It depends on the use case. For prototyping, we need a representative sample. For production, we work within your security and compliance requirements. We never retain your data.
How do you handle AI accuracy and hallucinations?
We design systems with validation layers, confidence scoring, human-in-the-loop checkpoints, and fallback paths. We set clear accuracy targets and measure against them.
What's the typical timeline?
2 weeks to a working prototype. 6-12 weeks for a production-ready AI feature. Ongoing iteration after launch. The prototype phase tells you whether the project is worth building.
Tell us what you're trying to automate
We'll tell you if AI is the right approach, what it would take, and how to prototype it in 2 weeks.