TIMELINE
4 weeks to the first agent in production.
Week 1: map. Weeks 2–3: build. Week 4: ship. If it isn’t shipping work for your team in week four, the engagement isn’t done.
Ofia is a senior team plus a proprietary platform. Send us your most painful workflow. We map your org, ship the first agent live in 4 weeks, and run the platform that holds your AI teammates together — personal to each role, aligned to your norms, connected across teams.
BCG sends a deck. We send a working agent — in three weeks, on the platform you keep.
13 systems in production. Open /work. Pick the one that looks like your company.
| Workflow | Before → After |
|---|---|
| Contract redline | 3 hrs → 6 min |
| IT provisioning | 2 days → <1 min |
| SDR triage | 0 → 40% of pipeline |
| Manager 1:1s | not agent-shaped |
4 weeks to the first agent in production.
Week 1: map. Weeks 2–3: build. Week 4: ship. If it isn’t shipping work for your team in week four, the engagement isn’t done.
A workspace shaped like your org.
Not a chatbot, not a dev framework. The Ofia platform holds your AI teammates together — personal to each role, aligned to your norms, connected across teams.
Senior builders who sit with you.
We map your decision rights, escalation paths, and judgment loops, then translate them into how your AI teammates actually work.
Today’s models are extremely smart. The catch: they’re generic. The fix isn’t a better model — it’s two more layers on top. Your tools and knowledge so the agent knows your specific deck, not a deck. And the relational layer — how you actually work, how your team coordinates, how decisions get made — so the agent fits the way your company runs.
Most AI tools today are racing to layer two. Ofia is built for layer three — onboarded like a new teammate, aligned across your whole org, tuned to how you actually work.
Thirteen are live. These three keep showing up on operator calls — GTM, Operations, Legal.
SDRs scanning LinkedIn and funding news manually. Signals go cold.
AI-detected signals went from 0 to 40% of pipeline in 8 weeks.
The SDR's morning starts with a queue of ready-to-send sequences they didn't build — enriched by Clearbit, scored against ICP, written against the brand voice in Notion.
→ /work#lead-intelligenceIT ticket. Two-day wait. Three follow-up Slacks.
Provisioning time: under a minute. Tickets dropped to near zero.
A Slack request — "add Sarah to Figma and #design" — kicks the agent into Figma admin and Slack admin via browser automation, against an encrypted credential vault, with non-standard requests routed to a human.
→ /work#it-provisioningLegal read every page of every contract. Bottleneck.
Three hours to six minutes per contract. Legal reviews flagged clauses only.
A 28-page vendor contract gets clause-extracted, cross-referenced against the playbook in Notion, and redlined — uncapped liability flagged with replacement language drafted, every action logged for the auditor.
→ /work#contract-reviewRoles, decision rights, repetitive judgment loops, where information actually moves vs. where the org chart says it does. Half the work is finding the agent-shaped seams. The other half is naming what isn't agent-shaped yet — and saying so. The output is a one-page agent-systems map: which workflows convert cleanly, which don't, which three to ship in what order. It's a build spec, not a strategy doc. Your engineering team uses it on day one.
For a Series B SaaS company last quarter, the map named six workflows. Three were already agent-shaped (lead intelligence, contract review, IT access requests). Two needed a process fix first. One was a manager problem dressed up as a tooling problem — we said so on page one.
We don't build the platform first. We pick the workflow with the highest immediate payoff and the lowest political cost — the SDR triage queue, the IT provisioning queue, the contract redline pile — and ship that. Trust contracts cap blast radius before any code runs. Tri-tier review (builder, reviewer, human) gates every output. Every action traces back to its source. The first agent's job is to be politically undeniable, so the next three sell themselves.
The IT provisioning agent we shipped for an operations team took a Slack request from a hiring manager — “add Sarah to Figma and #design” — from a two-day ticket queue to under a minute. Tickets dropped to near zero in week three.
ChatGPT and Claude are personal assistants. LangGraph and n8n are developer frameworks. The Ofia app is something else: a workspace shaped like your company, with agents in it.
You deploy teams the way you already deploy teams. Spaces for departments, roles inside each space, reporting lines between them. The same primitives an operator uses to run people — applied to agents.
Institutional knowledge cascades. Company-wide rules at the top, department rules under those, team rules under those. Ingest from Notion, Confluence, or wherever that knowledge already lives — so the agents start out knowing what your people know, not what a generic model guesses.
Agents talk to each other under trust contracts. Sarah's analyst-agent can ask Marcus's ops-agent for a decision, with explicit policies on what's allowed, what needs approval, and what's never on the table. No more “I copied your prompt into my ChatGPT.”
It tunes to you. The longer it runs, the more it sounds like your company, respects the norms nobody wrote down, and makes the call your team would make. Six months in, it's not a vendor's product — it's your org's muscle memory.
See the Ofia app (60-sec walk-through) →
We're operators who got tired of watching AI engagements end in Confluence. We map the org, ship the first agent, and hand over the Ofia app — because that's the engagement we wish someone had run for us.
We're opinionated about the org map. If you want a yes-firm, we're not it.
Still have a question? contact@ofia.ai
One workflow per email. The messier the better. We reply within two business days with a one-page read on whether it's agent-shaped, which case study it rhymes with, and what the first four weeks would look like.