Eight offers, one practice. Start with the gap that is costing you the most — we will not sell you the other seven until they are useful.
01
AI Automation
Replace repetitive judgement calls with model-assisted workflows that still have an owner.
AI automation is for the work that is too messy for a simple if-this-then-that rule but too expensive for a person to do all day: classifying inbound mail, drafting replies from a knowledge base, extracting fields from PDFs, summarising calls into CRM notes, triaging support. We map the current process, mark where a human must remain in the loop, and only then introduce a model. The automation still lives in your CRM, middleware, or a small service we host — so you can see every run. Failures retry, alert, and fall back to a queue. We do not pretend a language model is a workflow engine.
02
AI Agents
Goal-directed agents that use tools — CRM, calendar, email, search — with explicit limits.
An agent is not a personality. It is a loop: observe, choose a tool, act, check. We build agents that can look up a contact, book a slot, update a deal, or fetch a policy paragraph, and we bound them with allow-lists. Multi-step agents get traces so you can see why they did something. We avoid open-ended “do my job” agents for production; those belong in a sandbox. Production agents have a single job, a success definition, and a human escalation path. That is how you deploy something your operations lead will trust on a Monday morning.
03
AI Chatbots
Site and WhatsApp chat that answers from your content and can hand off to a person.
Chatbots fail when they bluff. Ours retrieve from approved sources, refuse when they are unsure, and collect a lead or a ticket instead of hallucinating a price. We implement web widgets, WhatsApp, and help-desk sidekicks. The conversation is logged to your CRM. After-hours chat is how many companies cover demand without a call centre. We design the greeting, the qualifying questions, the booking handoff, and the analytics so you know containment rate versus deflection theatre.
04
AI Integration
Put models next to the software you already pay for, instead of adding another dashboard.
AI integration means the model is a citizen of your stack: HubSpot, Salesforce, or your CRM for contacts, Slack for approvals, Google Drive or Notion for files, Stripe for billing context, your database for facts. We handle auth, webhooks, and mapping. We also handle the unsexy contract: what happens when the vendor API changes, when tokens expire, when a field is empty. Integration is usually more of the project than the prompt. That is why we keep software development and AI in the same studio.
05
RAG Systems
Retrieval-augmented generation so answers come from your documents, with citations.
RAG is how you stop the model inventing policy. We chunk and embed your manuals, contracts, product docs, and FAQs; retrieve the right passages; and require the model to answer from those passages. We tune chunk size, metadata filters (brand, locale, product line), and re-ranking when recall is weak. Evaluations use a question set your team writes — real tickets, not trivia. Citations are shown to the user or stored for auditors. When the corpus is stale, retrieval is worse than a simple bot; we include an update path so adding a PDF is an operations task, not an engineering project.
06
AI Knowledge Bases
A living internal wiki the model can read — and your people still can.
A knowledge base that only a model can use will rot. We structure sources so humans edit in Notion, Git, Confluence, or a simple CMS, and the retrieval index follows. Ownership, review dates, and “source of truth” labels matter more than the vector database brand. We often start by turning the ten documents everyone already asks Slack about. Then we connect the assistant to Slack or the intranet. The win is time-to-answer for staff, not a moonshot “company brain.”
07
AI API Integration
Production wrappers around OpenAI, Anthropic, Gemini, and open models — with cost controls.
Calling a model from a prototype is easy. Calling it from production means retries, timeouts, streaming, prompt versioning, PII redaction, and budget caps. We build the API layer your app team can depend on: one internal endpoint, many providers if you want failover, logging for evals. We help you choose when a small open model is enough (classification, extraction) and when a frontier model is worth the invoice. You keep the keys in your account unless you explicitly want us to host.
08
AI Business Systems
End-to-end systems: intake, decision, action, and reporting — not a single widget.
An AI business system is what you have when chat, retrieval, automation, and a database share a design. Example: inbound lead → qualify → write CRM note → draft proposal section → notify sales. Another: support ticket → retrieve SOP → draft reply → human approve → send → close. We architect these as products with roles, audit logs, and SLAs. This is the closest thing to a “business OS” we will sell — because it is your OS, not a rented black box. It is also where our automation and software practices join the AI work so you are not stitching three vendors together.
