Services/AI Integrations
AI agents that do real work — not chatbots that pretend to.
Most "AI features" are a chat box bolted onto a sidebar. We build the other kind: agents that draft emails, triage tickets, summarise calls, generate reports, and quietly do the work nobody wanted to do. Production-grade, with guardrails, evals, and a paper trail.
Who this is for
Three kinds of teams we work with.
Ops teams automating triage
Inbound enquiries, support tickets, document review — the high-volume, low-judgement work that drowns your team.
Sales teams replacing manual outreach
Personalised first-touch emails, meeting prep briefs, pipeline updates — the work the SDR is too senior to enjoy.
Founders building AI-native products
Your product IS the AI. You need someone who has shipped agents to production, not someone who read the OpenAI cookbook last week.
What you get
Concrete deliverables. No vapourware.
The list below is what ships, not what we promise. Every project is quoted against this scope so you know exactly what you are paying for.
Custom agent
Claude or GPT, with tool use, scoped to your workflow.
RAG + knowledge base
Your docs, indexed, retrieved, cited.
Guardrails
Input validation, output filtering, hallucination checks.
Eval suite
Test cases that actually catch regressions before users do.
Audit log
Every prompt, response, and tool call — queryable.
Cost monitoring
Per-tenant token usage, budget alerts, model fallbacks.
Streaming UI
Real-time response, optimistic state, cancellable.
Wired to your stack
Slack, Gmail, your CRM, your CMS — wherever the work lives.
How we do it
A four-step path. Designed not to surprise you.
Use-case design
We pick the workflow with the highest leverage and the clearest success criteria.
Prototype + eval
A working agent in two weeks, plus the eval suite to prove it works.
Production hardening
Guardrails, monitoring, cost controls, fallbacks, retries.
Deploy + watch
Live behind a flag for a cohort, then rolled out as the metrics earn it.
Stack
Selected work
See it in production.
Includes
- check_circleOne production agent / workflow
- check_circleRAG over your knowledge base
- check_circleEval suite with regression tests
- check_circleAudit log and cost monitoring
- check_circle30-day tuning window post-launch
FAQ
Questions about ai integrations.
Everything we get asked at the start of a project. If yours is not here, just write to us.
A plugin is a chat box. We build agents that take actions — file a ticket, send an email, update a record, draft a document. The chat is the input surface, but the value is the work it actually does.
Both, depending on the job. We benchmark on your data and pick the model that wins on quality, latency, and cost. Most production agents end up using two or three models for different steps.
You cannot prevent them entirely. You design for them — strict output schemas, retrieval-grounded answers with citations, evals that catch regressions, and a human-in-the-loop for high-stakes actions.
We use zero-retention API endpoints from Anthropic and OpenAI by default. Your data is not used for training. For sensitive use cases we can deploy via Bedrock or Azure for region compliance.
Highly variable, but most agents we deploy run between R500 and R8 000 per month in API spend depending on volume. We instrument everything so you see cost per query, per tenant, per workflow.
Rarely the right answer. Frontier models with good prompting and RAG beat fine-tunes for almost every business use case in 2026. We will tell you honestly when fine-tuning is worth it (it usually is not).
Eval suites with golden answers, production metrics on success / failure / human-override rates, and business metrics (tickets closed, emails sent, time saved). We do not ship without the dashboard.
Related insights
Further reading from the workshop.
Practical writing on the topics this service touches — pricing, decision frameworks, ROI data, and the things that change once a project is live.