Start with the five areas we lead with. The supporting list below is the rest of the work those engagements often need — not a promise that every item is a standalone product.
Add language-model features to an existing product: copilots, document tools, content workflows, and API-backed assistants.
Useful when A partner has a client who wants AI inside a current product, but the in-house team should not pause other delivery to learn the stack.
Retrieval systems that answer from a defined knowledge base, with source tracking and a path to iterate on quality.
Useful when Consultants, support teams, or internal staff spend too long searching documents that already exist.
Forecasting, scoring, and classification models connected to the data and workflows a team already uses.
Useful when A project needs predictions or ranking, not only a chatbot, and the current team lacks ML delivery capacity.
Tool-using agents and automations that complete multi-step work, with human review on the steps that need it.
Useful when Repetitive operations, research, or hand-offs are eating delivery time that should stay with the partner’s client work.
Application, API, and integration work so AI features sit cleanly inside the product instead of as a disconnected demo.
Useful when The AI piece is only useful if it talks to the existing CRM, database, auth, or client portal — and that engineering still has to ship.
These sit underneath the primary work — useful when a project needs data, product, or automation around the AI piece.
Custom ML systems that learn from your data, from prototype through deployment and monitoring.
Neural networks for vision, audio, and other unstructured-data problems.
Systems that interpret images and video — detecting, classifying, and measuring what matters.
Extract meaning from text at scale: classification, entity extraction, summarization, and multilingual understanding.
Fine-tuned and orchestrated large language models tailored to a product’s domain, tone, and constraints.
Ground language models in a team’s own documents so answers can be traced back to source material.
AI agents that plan, use tools, and complete multi-step work with human oversight where it matters.
Assistants and chatbots that resolve routine questions and hand off when needed.
Systematic prompt design and evaluation so LLM output stays consistent and usable.
End-to-end data science work that turns messy datasets into models and insights teams can use.
Decision-ready analytics that connect metrics to the questions a team actually needs answered.
Dashboards and reporting that make complex data easier to review and act on.
Forecasting and scoring models that help teams plan from likely outcomes, not only past reports.
Statistical models that quantify uncertainty and test whether an effect is real.
Structured data collection from public sources, scheduled and delivered where a team needs it.
Custom software designed around how a product or internal process actually works — built, tested, and documented.
Marketing sites and web apps that load quickly, stay maintainable, and support conversion.
End-to-end web applications with robust backends, databases, and frontends — from prototype to production.
Help take an AI product from idea toward launch: scoping, architecture, embedded AI features, and the surrounding product work.
Maintainable Python systems for data platforms, APIs, and automation.
Documented, secure APIs with FastAPI — ready to integrate into an existing product.
Replace repetitive, rules-based work with automation that can also handle variation in documents and decisions.
Connect tools into automated workflows with error handling and visibility.
Share a short brief. We’ll tell you whether the work is a fit and how a collaboration could be scoped.