AI that ships into the product, not a slide deck about AI.
Custom AI solutions that help businesses automate processes, understand data, generate insights, and build smarter products and customer experiences.
Why teams come to us
- Manual and repetitive business processes
- Large volumes of data difficult to analyze
- Slow decision-making and limited business insights
- Time-consuming customer support and operations
- Difficulty personalizing customer experiences
- Products lacking intelligent/predictive capabilities
- High operational costs from inefficient workflows
What's delivered
AI-powered applications & features
Custom AI/ML models and integrations
Generative AI & LLM integrations
AI chatbots and intelligent assistants
Recommendation & personalization systems
Predictive analytics & forecasting
Document and data processing automation
Computer vision solutions where applicable
AI APIs and model integrations
Model evaluation, deployment & monitoring
Delivery phases
01
Discovery & feasibility
The specific problem and available data get assessed together — feasibility gets established before any model work starts, not after.
02
Design
Whether the solution is a custom model, a generative AI/LLM integration, or an existing AI API gets decided based on the actual problem and data, not a default choice.
03
Build & integrate
The AI feature — whether a chatbot, recommendation system, predictive model, or document-processing automation — gets built and integrated into the real product or workflow.
04
Evaluate & monitor
Model evaluation, deployment and ongoing monitoring close out the engagement — an AI feature that isn't monitored after launch tends to degrade quietly.
More detail
Is this the right fit?
- Manual, repetitive business processes are eating time that intelligent automation could handle.
- There's more data than the team can realistically analyze by hand.
- Decision-making is slow because the insight isn't surfaced from the data that already exists.
- Customer support or operations work is time-consuming and could be partially automated.
- The product needs a predictive or personalized capability it currently doesn't have.
Not the right fit if
- A project that wants "AI" as a marketing label with no specific business problem behind it.
- A use case with no existing data to ground or evaluate a model against.
- A team that hasn't yet validated the underlying product the AI feature would sit inside — that's MVP Development first.
What we need from you
The specific business problem the AI feature should solve
"Add AI" isn't a scope — a specific process, decision, or experience it needs to improve is.
What data already exists to work with
Custom AI/ML models and predictive analytics are only as good as the data behind them — knowing what's available up front shapes what's realistic.
Where the AI feature needs to integrate
Whether this plugs into an existing product, a support workflow, or a standalone tool changes the integration work significantly.
Tech, and why it's the right call
Generative AI & LLM integrations
Used where a language-based feature — a chatbot, content generation, document processing — genuinely fits the problem, not applied as a default.
Recommendation & personalization systems
Built around real usage data, when it exists, rather than assumed rules about what users want.
Predictive analytics & forecasting
Applied to genuine decision-making problems — inventory, churn, demand — where a prediction actually changes what the business does next.
Model evaluation, deployment & monitoring
Treated as an ongoing part of the deliverable, not a one-time launch — a model's performance can drift as real-world data changes.
Related case studies
Guides
- Predictive Maintenance: Start With Thresholds, Not ML
How to start IoT predictive maintenance: collect clean data, set thresholds, add simple analytics and only then machine learning. What the guides agree on.
- Single Agent or Multi-Agent? Start With the Simplest Design
Anthropic's own guidance on workflows, single agents and multi-agent systems: when extra agents help, what they cost, and how to test before you build.
- Internal AI Assistant: How to Avoid Leaking the Wrong Data
Enforce permissions in the retrieval layer, not the prompt. OWASP's guidance for internal AI assistants, plus a short test you can run before launch.
FAQ