Six ways we put AI into production.
Most engagements start with one of these and grow. Each links to a page covering scope, process and typical timelines.
AI Agents & Automation
Autonomous agents that plan, call your tools and APIs, and complete multi-step work rather than just answering questions.
Explore AI Agents →Generative AI & LLM Apps
RAG assistants, content engines and copilots grounded in your own documents and data, with retrieval you can audit.
Explore Generative AI →Machine Learning & Predictive
Forecasting, recommendations, scoring and anomaly detection, trained on your data and monitored once deployed.
Explore Machine Learning →AI Chatbots & Assistants
Support and sales assistants that resolve real queries, hand off cleanly to humans, and stay inside your policies.
Explore AI Chatbots →Computer Vision
Detection, classification, OCR and quality inspection for images and video, deployed to cloud or edge.
Explore Computer Vision →AI Strategy & Consulting
Find the high-ROI use cases, de-risk them with a proof of concept, and get a roadmap you can actually fund.
Explore AI Strategy →AI that survives contact with production.
Most AI projects die between the demo and the deployment. Our process is built around that gap.
- Evaluation before launch. Every model or agent ships with a reference dataset and scored evaluations, so you know what accuracy you are getting before users do.
- Grounded in your data. Retrieval, fine-tuning and prompt design are built on your documents and systems, not generic web knowledge, so answers are specific and traceable.
- Model-agnostic by design. We select from OpenAI, Anthropic, Gemini and open models like Llama and Mistral per use case on accuracy, latency and cost, and swap as the frontier moves.
- Guardrails and monitoring. Rate limits, output validation, escalation paths and logging come as part of the build, not as a later hardening project.
AI Development Services, answered.
Where should we start with AI?
If you have a clear use case, start with a proof of concept: two to three weeks to prove feasibility on your real data. If you do not, start with an AI consulting engagement, which typically runs two to four weeks and ends with a prioritized roadmap and a firm estimate.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An agent takes action. It plans, uses tools and APIs, and completes multi-step tasks toward a goal. We build both, but agents are where the operational leverage usually is.
Do we need our own data to start?
For agents, RAG assistants and predictive models, yes, your data is what makes the result yours rather than generic. For a first proof of concept, a representative sample is usually enough to prove the approach.
Which model provider do you use?
Whichever fits the use case. We are model-agnostic and benchmark options on your actual task for accuracy, latency and cost. That choice is documented so you can revisit it as pricing and capability change.
How do you stop the model making things up?
Grounding, evaluation and guardrails. Answers are retrieved from your sources with citations, outputs are validated against expected formats, and we run scored evaluations against a reference set before launch and on an ongoing basis after.
Have an AI use case in mind?
Tell us what you want to automate and we will tell you honestly whether AI is the right tool.