AI & ML Solutions
AI that ships to production, not just to a demo
We build LLM applications, retrieval-augmented chatbots, agents and ML pipelines with the evaluation, security and cost controls that production systems need.
What we do
What we offer
LLM apps, RAG chatbots, AI agents and ML models that solve a measurable business problem and run reliably in production.
LLM & GenAI applications
Copilots, content tools and assistants built on leading foundation models with guardrails in place.
RAG chatbots
Assistants that answer from your own documents and data, with citations and access control.
AI agents
Agents that call your tools and APIs to complete multi-step tasks, with human approval where it matters.
Machine learning models
Forecasting, classification and recommendation models trained, deployed and monitored.
Document intelligence
Extract, classify and validate data from invoices, forms, contracts and reports at scale.
AI integration into existing products
Add AI features to your current web or mobile product without rebuilding it.
Deliverables
What you get
- Use-case assessment and ROI estimate
- Evaluation dataset and quality benchmarks
- Production API or embedded AI feature
- Prompt, model and retrieval configuration in version control
- Cost, latency and quality monitoring dashboard
- Security and data-handling documentation
Technology
Tech we use
Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
Frameworks
- Python
- LangChain
- LlamaIndex
- FastAPI
- PyTorch
- scikit-learn
Retrieval
- pgvector
- Elasticsearch
- Pinecone
- Qdrant
MLOps
- Docker
- AWS SageMaker
- Azure AI
- MLflow
Process
How we deliver
Use-case validation
We confirm the problem is a good fit for AI and define what 'good enough' means in numbers.
Proof of concept
A focused prototype on your real data in two to four weeks, measured against the benchmark.
Production hardening
Evaluation suites, guardrails, access control, caching and fallbacks for reliability and cost.
Monitor & improve
Ongoing tracking of quality, latency and spend, with regular tuning as models and data change.
A US insurance-tech platform
A configurable rule engine that cut product launch time for an insurer
- Time to change a product rule
- Weeks → days
- Decisions traceable to rule versions
- 100%
Is our data safe when using LLMs?
We use enterprise API terms that exclude your data from model training, keep retrieval indexes in your own cloud, and apply role-based access so users only see answers from documents they are allowed to read.
How do you measure whether an AI feature works?
Every project starts with an evaluation dataset built with your team. We track accuracy, groundedness, latency and cost per request, and we don't ship until the agreed thresholds are met.
Which model should we use?
It depends on the task, latency budget, cost and data residency. We benchmark two or three candidates on your data and recommend one, and we keep the architecture model-agnostic so you can switch later.
Can you add AI to our existing product?
Yes, and it's often the fastest path to value. We integrate through your existing APIs and UI, starting with one high-impact workflow.
Let's talk about your ai & ml project
Tell us what you're building. A senior engineer will reply within one business day with questions, ideas and next steps.