01 / Treasury
Multi-Entity Treasury Consolidation.
Three banks, three entities, three currencies — reconciled into one canonical sheet.
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
People + AI
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.
Sequoia AI Ascent 2026 · Closing keynote
AI can do the work. AI will do the work. But only the human connection can give you a reason to care.
Where We Came From
We started inside operations.
We never left.
Inside view01 / DEEPEST
Observed patterns02
AI Fluency03
Human-in-the-Loop04
Autonomous Agents05
05Autonomous Agents
04Human-in-the-Loop
03AI Fluency
02Observed patterns
01Inside view — 10 years of ops
01Inside view
02Observed patterns
03AI Fluency
04Human-in-the-Loop
05Autonomous Agents
LayerHover a layerEach layer reveals what it adds to the system — from a decade inside client operations up to autonomous agents running in the background.
2016 — Year OneWe embedded inside operationsDigital Ops first, then Customer Service, then Sales. 60+ companies. The job was always to learn how the work actually ran, then ship it, then keep shipping it.
How We Run It
3 Pillars.
One operating system.
AI Fluency
Trained on
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.
Blueprints
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.
02 / FP&A
Monthly P&L Anomaly Scan.
Three months of expenses re-fetched and scored across nine dimensions, every month.
03 / AR & Collections
Wire-to-Invoice Reconciliation.
Wires matched to open invoices in seconds. FIFO on multi-match, exceptions flagged for review.
04 / AR & Collections
Owned AR Runtime.
Two systems chasing the same invoice, replaced by one runtime that ends the duplication.
See every blueprint.
All workflows, all dimensions.
BROWSE BLUEPRINTSThe Build Floor
Built on engineering.
Promotable to your cloud.
Pure LLM is brittle. Pure scripts can't think. The marriage is what ships.
Python
Deterministic orchestration. Retries, validation, guards — the bones nothing else has.
LLM
The thinking layer — free-text reasoning, classification, judgment. Where the model earns its keep.
Cloud
Promotable day one — GCP, AWS, anywhere. Yours to host, not ours to ransom.
No low code.
The Landscape
The corner the market
is moving toward.
Operational knowledge on one axis. AI capability on the other. For 10 years the top-right corner sat empty because nobody could be in 2 places at once. Sequoia is naming the shape now. The companies that ship the work, drawn from inside the work, are what this cycle rewards. We've been there since before there was a name for it.
AI CapabilityOperational Knowledge
AI Consultancies
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.
HKR.AI · The Open Quadrant
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.
Off-the-shelf AI Tools
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.
Traditional Staffing
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.
Get Started
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.