Start with the work, then choose the AI
We map the task, users, and desired outcome first. A focused prototype helps establish whether an AI feature adds value before expanding the build.
AI that fits
the way you work.
A helpful assistant, answers from your own knowledge, or a workflow that takes care of the repetitive steps. We design and implement AI features around what your business actually needs — in a new product or alongside the website, application, and systems you already have.
01 / LLM-POWERED EXPERIENCES
LLM-powered experiences
Large language models (LLMs) can help people ask questions, summarize information, draft content, and turn unstructured input into useful data. We build the interface and supporting services around a specific task, your brand voice, and the people using it — from a website assistant to a tool inside your customer portal.
02 / RAG & KNOWLEDGE SEARCH
RAG & knowledge search
Retrieval-augmented generation (RAG) brings relevant information from your approved content into an AI response. We can connect product information, help articles, internal documents, or other agreed sources, with source references and access controls. Content refreshes, retrieval quality, and responses when an answer is missing are part of the design.
03 / MCP & CONNECTED TOOLS
MCP & connected tools
Model Context Protocol (MCP) gives compatible AI applications a standard way to connect to tools and data. We can build or integrate MCP servers so an assistant can look up information or request a defined action in your systems. Permissions, input validation, and approval steps are scoped around what each user should be able to do.
04 / AI WORKFLOW AUTOMATION
AI workflow automation
Connect AI to the work around your website or application: classify incoming requests, extract details from documents, prepare a reply, or route information into a CRM. We combine model calls with ordinary APIs and background jobs, including human review where needed, retries, and a clear record of what happened.
A GOOD FIT WHEN
Sound
familiar?
- Add a product or support assistant to an existing website
- Help staff find answers across approved documents and internal knowledge
- Summarize activity and draft responses inside a customer portal
- Turn form submissions or uploaded documents into reviewed, structured records
- Connect a compatible AI assistant to business tools through MCP
- Build a new application with AI as one purposeful part of the experience
WHAT YOU GET OUT OF IT
Fit your existing product
We review your codebase, CMS, APIs, authentication, and data access. Features can live in the current interface with supporting services behind it, or be planned into a new build. We explain platform constraints before proposing a larger change.
Shape it around your business
Agree on the content, tone, permissions, integrations, and review steps that suit your customers and team. We choose the model and architecture around quality, response time, operating cost, and data requirements.
Measure, launch, and improve
Test representative tasks, missing information, and failure cases before a staged launch. We plan useful monitoring, usage limits, maintenance, and handover so the feature has an owner after release.
OUR 4D PROCESS
A little structure.
A lot of collaboration.
- 01
Discover
Understand your business, your users, and what a useful result looks like.
- 02
Design
Shape the experience, explore the details, and test the direction together.
- 03
Develop
Build the pieces, connect your systems, and validate how everything works.
- 04
Deliver
Deploy thoughtfully, support the handover, and plan what comes next.
COMMON QUESTIONS
Asked, and
answered.
Can you add AI to our current website or app?
Often, yes. We review your platform and available integrations first. A feature might be added to your existing interface with an API or a separate supporting service; a full rebuild is not automatically needed. The approach depends on access, platform limits, and your goals.
Do we need a chatbot, RAG, MCP, or all three?
No. They solve different parts of a problem. A chatbot is an interface, an LLM processes language, RAG supplies relevant knowledge, and MCP connects compatible AI applications to tools and data. We select only the pieces your use case needs; a direct API integration or conventional automation may be enough.
Can the AI take actions in our systems?
Where the platform allows it, we can define specific actions such as preparing a record or routing a request. We agree on permissions and validation, and can require a person to approve an action before it changes a system or sends a message.
How do you handle private data and incorrect answers?
We scope what data the feature can access and which providers may process it, then design access controls, source references where appropriate, and a fallback or human handoff. AI responses can be wrong, so we test representative examples and agree on review requirements for the task.
What does it cost to run?
Running costs depend on the model, request volume, amount of content, hosting, and connected services. We estimate those costs during scoping, then plan limits and monitoring around your budget. Ongoing usage and support are discussed separately from the implementation.
KEEP EXPLORING
NEXT / YOUR IDEASomething in mind?
Something in mind?
Let’s open it up.
A clear brief or a scribble on a napkin. Tell us what you need, even if you’re still figuring it out.
Tell us about your project Or schedule a free 20-minute consultation