AI products & proofs of concept

AI that ships and pays for itself

From enterprise products to rapid proofs of concept, we build production-ready AI and prove its value fast — so you see it working before you invest.

Discuss Your Project How We Work
  • 25+ POCs Delivered
  • 12 Industries
  • 2–4 Weeks to Demo
  • Your Data Stays Yours
AI assistant conversation next to an hours-saved chart and ROI figures
Featured work

Problems we've turned into working products

A few recent builds — what was broken, what we shipped, and how long it took.

RAG • Knowledge

AI Document Assistant

Problem: An internal team was spending hours each week searching through hundreds of policy documents to answer recurring questions.

Solution: A retrieval-augmented assistant indexed on their document library. Answers cite the exact source paragraphs and stay grounded in the team's actual content.

  • 4 weeks · 2 engineers
LLMs Vector Database RAG Cloud Deployment
Agents • Analytics

Sales Insights Agent

Problem: Sales leadership had data scattered across the CRM, spreadsheets, and email exports. Weekly reporting was manual and error-prone.

Solution: An AI agent that connects to those sources, answers questions in plain English, and drafts a weekly pipeline summary.

  • 6 weeks · 1 engineer + PM
LLMs Agents Spreadsheet APIs CRM Integration
RAG • Productivity

Customer Support Copilot

Problem: Support agents were answering the same questions across channels and copying from outdated docs.

Solution: A copilot that drafts responses from past tickets and the help center. Agents review and send — never auto-replies.

  • 5 weeks · 2 engineers
RAG Vector Search Workflow Automation
Embeddings • Onboarding

Internal Knowledge Search

Problem: New hires were taking weeks to ramp because the answers they needed were spread across Notion, Drive, and Slack.

Solution: A unified search trained on internal documentation, with access control respected at retrieval time.

  • 5 weeks · 2 engineers
LLMs Embeddings Access Controls
Vision • E-commerce

Visual Catalog Tagging

Problem: A small e-commerce team had thousands of product photos with inconsistent or missing tags, hurting search.

Solution: A vision model that suggests tags and short descriptions; a reviewer queue keeps the team in the loop.

  • 3 weeks · 1 engineer
Vision Models LLMs Reviewer Workflow
Audio • Productivity

Meeting Notes Automation

Problem: A consulting team was losing context between meetings. Notes were inconsistent and action items got lost.

Solution: Automated transcription with a structured summary, action items, and follow-up draft emails.

  • 3 weeks · 1 engineer
Speech-to-Text LLMs Email Integration
Why a POC first

De-risk your AI investment

Rapid Validation

Get a working prototype in 2–4 weeks. Test assumptions and validate business value quickly before major investments.

Cost Efficient

Minimal investment to prove concept. Scale only after you've seen it work with your actual data and workflows.

Real Data Testing

We build with your actual data and workflows. No generic demos — real solutions designed for your real problems.

Production Ready

POCs built with production architecture in mind. Seamless transition from proof-of-concept to full product.

Proofs of concept

Delivered AI solutions

Multi-agent systems, retrieval and automation — built on the frameworks we teach.

LangGraph

Healthcare Navigator

Multi-Agent Patient Assistance System. Three coordinated agents that help patients find the right care, verify insurance, and book appointments.

  • Symptom Assessment
  • Insurance Verification
  • Provider Matching
  • Appointment Scheduling
LangGraph 3 Agents GPT-4 RAG
CrewAI

Legal Document Analyzer

Contract Intelligence System. A multi-agent crew that reviews contracts, extracts key clauses, flags risks, and drafts compliance reports.

  • Clause Extraction
  • Risk Assessment
  • Compliance Check
CrewAI Claude Vector DB
MCP & A2A

Enterprise Agent Network

Agent-to-Agent Communication. Google's A2A protocol in action — specialized agents communicating across enterprise systems.

  • Cross-System Integration
  • Agent Orchestration
  • MCP Tools
  • A2A Protocol
MCP FastAPI AutoGen
AutoGen

Financial Analyst Team

Conversational Multi-Agent Analysis. A team of analysts that research markets, analyze trends, and produce investment insights together.

  • Market Research
  • Trend Analysis
  • Report Generation
AutoGen GPT-4 Python
Computer Vision

Laundromat Automation

Vision-Powered Quality Control. Vision system for dry cleaners that automates garment inspection, stain detection, and quality checks.

  • Garment Recognition
  • Stain Detection
  • Quality Scoring
GPT-4V OpenCV YOLO
CrewAI

HR Recruitment Crew

Automated Hiring Pipeline. Specialized agents handling resume screening, candidate matching, interview scheduling, and feedback.

  • Resume Parsing
  • Skill Matching
  • Auto-Scheduling
CrewAI RAG Zapier
LangGraph

Retail Shopping Assistant

Personalized Commerce AI. Conversational assistant that learns preferences, recommends products, and handles comparisons and orders.

  • Product Discovery
  • Personalization
  • Order Tracking
LangGraph Commerce API Claude
AutoGen

Supply Chain Optimizer

Intelligent Logistics Management. Agents that monitor inventory, forecast demand, optimize routes, and automate supplier updates.

  • Demand Forecasting
  • Route Optimization
  • Vendor Management
AutoGen Python APIs
LangChain

Real Estate Advisor

Property Intelligence Bot. Analyzes listings, compares market data, scores investments, and schedules viewings for buyers.

  • Market Analysis
  • Property Comparison
  • Investment Scoring
LangChain RAG GPT-4
Our stack

Tools we reach for

LLMs & APIs

OpenAI, Anthropic, open-source models

Vector Databases

Pinecone, pgvector, Qdrant, Weaviate

Frameworks

LangChain, LlamaIndex, custom orchestration

Cloud

AWS, Azure, GCP — wherever your data lives

Eval & Monitoring

LangSmith, custom dashboards, logging

Frontend

React, Next.js, Tailwind for product UIs

Industries we've built for

Education Finance Healthcare HR & Recruiting Legal Logistics Manufacturing Marketing Real Estate Retail
How we build

How we approach the build

  1. 01

    Start small, measure honestly

    Every project begins with a sharp scope and a clear way to measure success. If we can't measure it, we don't build it.

  2. 02

    Working software each week

    You see a demo every week — not status decks. Course correction is built into the rhythm.

  3. 03

    Your data, your control

    We don't train on your data. Everything stays in your environment unless you choose otherwise.

  4. 04

    Built to hand off

    Clean code, documentation, and a session walking your team through how it works. No lock-in.

No NDA needed for the first conversation

Have an idea? Let's stress-test it.

Send us a paragraph about the problem. We'll reply with whether AI is the right tool, and a rough sketch of how we'd approach it.

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