The work was always the product.
We never sold software. We sold the work. The model got good. Same job, fewer hands.Managed AI Operations: the People who run the work, plus the AI we built around it.Earned across 10 years of running yours.
Built on a decade of work with
Sequoia just named
what we've been doing for 10 years.
Earlier this year, Konstantine Buhler closed Sequoia's AI Ascent keynote with the line we wish we had on the wall in 2016. The argument: the model can do the work, the model will do the work, and the reason anyone cares about the outcome is still human.
AI can do the work. AI will do the work. But only the human connection can give you a reason to care.
We started inside operations.
We never left.
3 Pillars.
One operating system.
AI Fluency
Capability before delivery means education. Our domain operators train on Anthropic's Claude as their day-to-day driver, alongside general AI guidelines, through a continuous program on our internal LMS. We call this our Forward-Deployed Operator: a domain operator, not an engineer, AI-fluent and tool-ready. AI Fluency produces the operators who run Human-in-the-Loop and Autonomous Agents.
Human-in-the-Loop
The default. Where most of the value lives. AI drafts, retrieves, summarizes, ranks, classifies. A Forward-Deployed Operator reviews and decides on anything that touches money, customer relationship, judgment, or the irreversible. Refunds, escalations, edge-case calls, anything you can't take back in a day. The operator is the accountable party; AI scales the work, the human owns the outcome. Every loop they run deepens Institutional Knowledge, so the system gets smarter on your specific operation, not on a generic dataset. Priced in credits against outcomes confirmed by humans.
Autonomous Agents
Selective. Earned, not assumed. End-to-end automation only on surfaces that have proven safe under HITL: well-defined work, reversible actions, low-stakes errors. A surface graduates to autonomous after observed accuracy holds. Drift, errors, and edge cases route back to a human queue. Autonomy here is conditional, monitored, and bounded.
How we map the work,
and how you could too.
A Blueprint is a workflow we've actually run, written down in enough detail to rebuild. Tools, handoffs, exception patterns, what's automatable, what isn't. We use them to deliver. You can use them to see what's possible inside your own operation.
Multi-Entity Treasury Consolidation.
Three banks, three entities, three currencies — reconciled into one canonical row, every hour.
Read blueprintMonthly P&L Anomaly Scan.
Nine anomaly factors scored every month, Slack summary plus markdown deep-dive — so the read on the 18th takes ten minutes.
Read blueprintWire-to-Invoice Reconciliation.
Bank statements parsed on arrival, every wire matched against open invoices — auto-post the clean ones, escalate the rest, keep the audit trail.
Read blueprintOwned AR Runtime.
Two systems chasing the same invoice, replaced by one runtime that ends the duplication.
Read blueprintSee every blueprint.
All workflows, all dimensions.
BROWSE BLUEPRINTSBuilt on engineering.
Promotable to your cloud.
Pure LLM is brittle. Pure scripts can't think. The marriage is what ships.
Deterministic orchestration. Retries, validation, guards — the bones nothing else has.
The thinking layer — free-text reasoning, classification, judgment. Where the model earns its keep.
Promotable day one — GCP, AWS, anywhere. Yours to host, not ours to ransom.
The corner the market
is moving toward.
Operational knowledge on one axis. AI capability on the other. The top-right corner only opened when AI got good enough to do real work — and the scarce half isn't the AI, it's the operational knowledge to point it. That half we've had for a decade. Sequoia's naming the shape now. We've been building it since before it had a name.
Strong models. No hands inside the business.
Slide decks, pilots, and engineers who've never run your operations. They build to a spec, hand it over, and hope it sticks. When they leave, the knowledge walks out with them.
Embedded teams plus a custom AI layer.
Managed AI Operations. AI agents trained on Institutional Knowledge we built running operations from the inside, run by AI Managers who own the SLA. Human-in-the-Loop on anything that matters. Autonomous Agents only where the work earns it. The vendor takes the blame, not you.
Generic capability, generic fit.
Powerful models wrapped in a SaaS. One-size templates that are always one layer away from how your company actually runs. Good enough for demos. Never enough for operations.
Deep knowledge. No AI layer.
People who understand the work. But the workflows sit in their heads and cannot compound. You scale the team by adding more bodies, not by making each hour worth more.
Start with one workflow.
See the result before you sign anything bigger.
A Forward-Deployed Operator embeds in the work, captures it, and proposes how to deliver it. New clients run a short embed. Existing clients skip it. We're already there.
We put a domain operator inside your team, not an engineer. They shadow the work, run the tools, learn the edge cases. If you already work with HKR.TEAM, this step is done.
Your workflow gets mapped into Institutional Knowledge: how the work actually runs, including the exceptions. We propose what AI should do (with your operator in the loop) and what it can do alone (only where the work is bounded and mistakes are recoverable). You approve the plan. First piece ships in weeks, priced in credits.
A senior HKR operator watches every live workflow. Confidence earns autonomy. Drift goes back to a human. Your Institutional Knowledge keeps growing, so the system keeps getting sharper on your operation. Generic agents plateau. Ours compound.
Pay per Outcome.
Your data stays yours. Siloed and encrypted.