Workflow Automation
Our flagship, done-for-you engagement. We find the highest-value wins, build the foundations they depend on, then build the AI agents on top — founder-led from first call to handover.
01 / The engagement arc
Get the plan right
A free 30-minute call where we get a read on your business — what you do, the pain points you're feeling, and the problems you're hoping to solve with AI and automation. You leave knowing whether there's a fit, no pitch and no obligation.
A thorough two-to-four-week engagement with the owners and key stakeholders. We get deep on how you actually work, then deliver your AI Implementation Roadmap: your biggest wins, the foundational elements each one needs, the order to build them in, and a defensible business case for each.
Build it right
We execute the roadmap in two layers. First the data and infrastructure foundations — the groundwork everything depends on. Then the AI agents that run on top of them: the parts that actually do the work, solving the pain points from your discovery call. Founder-led throughout, with documentation and handover so you own it after we leave.
AI agents
What runs on top and does the actual work
↑ built on ↑
Data & infrastructure foundations
The groundwork everything depends on
For owners and leaders who prefer a full white-glove service, we look after all of the ongoing maintenance — keeping your AI automations monitored and continually fine-tuned so they go on performing the way they're meant to. Complete peace of mind that what we built keeps earning its place.
02 / What we build
Two layers — the data and infrastructure foundations, and the AI agents that run on top of them. 18 capabilities in all; the audit tells you which — usually a handful, almost never all of them.
The substrate everything runs on — cloud, platform, security, observability, plus the AI runtime agents need to ship reliably.
Cloud strategy, regions, residency, environments, network. The choices you can't easily reverse.
Warehouse/lakehouse, object storage, containers, orchestration, streaming. Right-sized for stage.
IAM, SSO, secrets, encryption, classification, POPIA / GDPR — what auditors actually inspect.
Unified logs/metrics/traces, SLOs, on-call, FinOps. Where most platforms haemorrhage money.
Model gateway, prompt/policy guardrails, vector store & retrieval pipeline (RAG), structured outputs, eval infra. Built once.
Tested DR runbooks, verified restores, change management, incident response, deprecation.
Trustworthy data on the infrastructure, the analytics that turns it into decisions, and the access layer that lets agents query it safely.
Domain models, warehouse vs. lakehouse, layered design, source-of-truth decisions.
Ingestion, transformation, freshness SLAs, reconciliation against source systems.
Plain-language definitions, lineage, owned KPIs — same number in every tool.
Classification, IAM, audit logs, retention. POPIA / GDPR / regulator-ready.
Typed, governed, scoped views exposed to agents — including the corpus RAG retrieves from. Same controls that protect humans.
Decision-driven dashboards, embedded analytics, self-service where it earns its place.
Agents designed, built, and instrumented to run on top of the foundation — applied where AI genuinely earns its place.
Where AI belongs vs. traditional code. Four-question test, build-vs-buy, prioritised roadmap.
Scope, tools, decomposition, prompts, retrieval strategy (RAG, reranking, grounding), memory, multi-turn flow.
Implementation, source-system wiring, CI/CD, rollout — not just a chat window.
Golden sets, LLM-as-judge, regression, shadow runs, red-teaming, sign-off criteria.
Tracing, sampled scoring, drift detection, PII controls, audit trails, kill switches.
Training, RACI, change management, retraining cadence — capability that stays after we leave.
Want the full menu of agents we build? See AI Agents by business function.
03 / Example engagements
Composite illustrations of the kinds of roadmaps we deliver. Same audit. Different conclusions. Different work.
Mid-market FMCG retailer
Years of warehouse sprawl. Three definitions of “active SKU.” Considering an AI demand-forecasting agent.
Findings: data trust is the bottleneck. AI is premature.
What we'd build
Series B SaaS platform
Solid data team, clean stack. Wants AI agents in support and sales workflows.
Findings: foundation is fine. Skip data work; build AI properly.
What we'd build
Mining operator, multi-site
Compliance pressure. Cost overruns on the data platform. No appetite for AI yet.
Findings: infrastructure first. Re-platform, then revisit.
What we'd build