Agentic AI workflows that replace manual admin, production SaaS shipped fast, and MCP integrations into the tools you already run, with human oversight on every step and tiered model selection that keeps costs honest.
Four services, one team. AI does the heavy lifting; a senior lead reviews every output before it touches your business. Nothing ships without human sign-off.
Autonomous agents that replace stitched-together Zapier or Make rules. Tiered model selection, MCP integrations into Outlook, Xero, SharePoint, CRMs, judgment, not just triggers.
Idea to production SaaS without agency overhead. Spec, architecture, then AI-assisted delivery on cost-optimised infra.
Audit current-state workflows, prioritise the painful ones by pain and payoff, then architect the agent fabric and MCP connectors into the tools you already run, Outlook, SharePoint, Xero, CRMs.
Every workflow ships with a versioned config: which model handles which step, which MCP servers it can talk to, where guardrails live. Boring is good. Boring is auditable.
# tier the work, frontier model only when judgment matters
[agent]
name = "variation-handler"
goal = "draft variations, update finance, await review"
guardrail = "never_send_without_human"
[steps.reason]
model = "claude-opus-4"
input = "inbox.email"
[steps.draft]
model = "open-weight/llama-3-70b"
tools = ["sharepoint", "xero"]
[mcp.servers]
outlook = "mcp://office365"
sharepoint = "mcp://sp.northgate"
xero = "mcp://xero.payables"
A short, honest loop. We start in your real workflows, choose the right model on every step, and put a working system in front of users fast, then keep tightening the loop.
We sit with the people doing the work. Quotes, claims, support tickets, ops handoffs, we map current state, find the steps that break, and rank them by pain and payoff.
Frontier models for reasoning, open-weight models for routine drafting, plain rules for calculations. MCP connectors plug AI into the tools you already pay for, no rip-and-replace.
AI-assisted development takes us from spec to a production SaaS or site fast. We measure hours saved, error rate and cost per task, then keep tightening the loop.
One on a working business replacing manual admin with autonomous agents. One on a green-field SaaS shipped on AI-assisted delivery.
We mapped the nine workflows that run the business, quotes, variations, progress claims, supplier comparisons, site meetings, permits, subcontractor coordination, client updates, handover docs, and replaced the worst three with agentic workflows.
Business analysis, user flows and functional spec come first; then architecture and stack picks tuned for cost, Next.js, managed Postgres, edge functions, Stripe, Clerk. AI coding tools accelerate build under senior review, so clients get a production-grade SaaS in a fraction of a traditional agency engagement.
We pick from the same shortlist serious AI teams use today, and tier them by cost so the ongoing spend stays predictable.
We came to David for a website. He quickly showed us we were missing big opportunities beyond that and walked us into a smarter strategy. The workflow audit alone changed how we run admin, variations and progress claims that used to eat days now happen with the agent doing the heavy lifting.
Senior thinking from the first call. We got a spec that actually held up under engineering review and a SaaS in production in a fraction of the time other agencies were quoting, without the usual hand-waving about "AI accelerating things."
30-minute call, no pitch, bring a workflow, a SaaS idea, or a website that's not pulling its weight. You'll leave with a sharp opinion and a sketch of what we'd build next.