Product · Deskflow
Deals in. Audited verdicts out.
It reads the deal's documents, applies the rulebook you already wrote, and returns a verdict with the reasoning attached.
Only the exceptions reach your team. Everything else ships with a verdict, concerns, and recommendations attached.
In production on about 1,000 deals a week.
VERDICT · DEAL 4471
- VIN cross-matched across 5 documents PASS
- Lien payoff verified against bank letter PASS
- Odometer vs vehicle history PASS
- Name: title vs government ID FLAG
CONCERN · MEDIUM SEVERITY
Title reads JOHN SMITH; ID reads JOHN SMITH JR. The attached one-and-the-same affidavit is not notarized.
Needs human review: suffix bridge without a notarized affidavit. Everything else verified.
decision in 28s · rules + AI evaluator · reasoning stored
Not document processing. An operator.
Every high-volume operation has the same shape: deals arrive with documents and data attached, someone checks them against the rules, makes a call, and moves the deal forward. Deskflow does that job.
Deskflow receives documents and structured data, recognizes 46 document types across 13 categories, applies your company-specific rules and the institutional knowledge of your best people, makes the decision, and executes the workflow end to end. When something falls outside the rules, it escalates to a human with full context attached.
Extraction tools stop where the real work begins: the judgment call. Deskflow is built for the judgment call. Documents are just the input layer.
Under the hood, two systems check every deal. A deterministic rules engine applies the rulebook you already wrote: your policies, 52 preloaded US jurisdictions, your thresholds. An independent AI evaluator then reviews the full deal and catches what the rules didn't anticipate. The rules decide; the AI tells you when it doubts. Every verdict ships with its concerns and recommendations attached.
What Deskflow is not
One pipeline, seven stages
Every deal moves through the same pipeline. Most complete without a human touching them; the rest arrive at your team pre-analyzed.
- 01DocumentsRecognizes 46 document types across 13 categories, from titles to lien releases
- 02ExtractionReads and structures every input
- 03VerificationCross-checks sources against each other
- 04RulesApplies your rulebook plus 52 preloaded US jurisdictions: lienholder vs owner states, plate rules, ELT
- 05DecisionReturns a verdict with concerns and recommendations, with reasoning
- 06 ExceptionEscalates edge cases to a human, with context
- 07ApprovalExecutes the outcome and closes the loop
Humans stay in the loop where they add value (on exceptions), not on every deal.
Four ways this shows up in your P&L
Operations leaders don't buy AI. They buy throughput, accuracy, and resilience. Each bucket below carries its own proof.
Fewer reviewers, same volume
One automotive client cut its review team from 12 people to 6 while processing the same ~1,000 deals a week. The other six moved from processing every deal to handling only the ones that need judgment.
Capacity without a night shift
Deal volume peaks overnight, 10PM–7AM ET, when no one is on shift. Deskflow processes that peak in real time; the team picks up the exceptions in the morning.
Errors down from 7% to about 1%
Manual review let roughly 7 in 100 deals through with an error. The most common catch now: a title reads JOHN SMITH, the ID reads JOHN SMITH JR, a mismatch a tired reviewer waves through at hour six.
The rulebook, not just the reviewers
52 US jurisdictions preloaded: lienholder states vs owner states, plate rules, electronic lien and title. The rulebook applies itself instead of living in one person's head.
Where Deskflow fits, and where it doesn't
About 80% of deals are clean: read the documents, apply the rules, done. The other 20%, deceased owners, LLCs, trusts, out-of-state titles, name changes, is what sets your cost structure. Deskflow is built for that 20%.
Strong fit
- ✓ Deceased owners, LLCs, trusts, out-of-state titles, and name changes show up every week, not rarely
- ✓ High deal volume: hundreds to thousands per week
- ✓ 3+ documents per deal, and the exceptions are the ones that cost you
- ✓ Business rules exist, whether written down or learned on the job
- ✓ 5–10+ FTEs spend their day on deals that are mostly clean until they aren't
- ✓ Errors have a financial consequence
- ✓ The hard 20% can be defined and routed, even if it can't be simplified
Not a fit
- ✕ Low deal volume, where the economics don't clear
- ✕ Decisions are mostly subjective judgment, not rules
- ✕ One-off, bespoke workflows that never repeat
We call $1.2M a year our threshold: labor cost, plus capacity blocked by the process, plus error and leakage, added up. Below that, the economics don't clear.
In production: 1,000 evaluations a week
A leading automotive marketplace processes around 1,000 vehicle evaluations per week, roughly 5 documents per deal, operated by a team of about 12 people. Each review took an experienced analyst around 20 minutes, and errors slipped through at about 7%. With Deskflow operating the pipeline, half of all deals are now auto-approved in under two minutes, and the team was redeployed from processing to exceptions and higher-value work.
~50%
of deals auto-approved in under 2 minutes
~70%
of the workflow AI-managed end to end
20 → 1–2 min
per review, for the deals that need a human
7% → ~1%
error rate across the process
12 → 6
people operating; the rest redeployed
10PM–7AM
overnight volume peak processed with no one on shift
Read the full cases:
Questions operators ask us
How does Deskflow learn our business rules?
Two ways. Explicit rules (policies, credit criteria, thresholds) are encoded directly. Learned rules are the judgment your senior operators apply without a manual. Name matching is the clearest example: the engine normalizes suffixes and initials, reorders "LAST, FIRST" to "FIRST LAST", and scores similarity against a 0.75–0.8 threshold, so "Mary Smith" and "Mary A. Smith" resolve as the same person instead of stalling a deal. That threshold, and the normalization rules behind it, are captured by working alongside your team during deployment.
What happens when a deal doesn't fit the rules?
It escalates. Deskflow routes exceptions to your team with the full analysis attached: what was checked, what passed, and exactly what triggered the escalation. Reviewers resolve edge cases in minutes instead of re-doing the whole review, and resolved exceptions feed back into the system.
How is this different from RPA?
RPA replays clicks. It needs clean inputs, stable screens, and breaks the moment a document looks different. Deskflow reasons over documents against rules stored in a database, plus an independent AI evaluator, and escalates exceptions with context. It captures the institutional knowledge behind a decision, which a script can't represent at all.
Does my team lose control of the decisions?
No. You set the boundaries. You define what the AI decides on its own, what requires human approval, and what always escalates. Every decision is logged with its reasoning, so audit and compliance see more per deal than they did with manual processing, not less.
What happens when the AI gets one wrong?
It gets caught, measured, and fed back. In our latest QA sample, 99 deals and 482 documents, document classification ran at 85% accuracy and deal-type accuracy at about 90%. We've root-caused the gaps and have a plan to bring both toward 96%. Anything the system is unsure about doesn't auto-complete: it escalates. Every verdict is logged with its exact inputs and reasoning, so a human can audit any decision after the fact. You see the error rate; you don't take it on faith.
How do I know it's still judging well six months in?
Because we monitor the judgment itself, not just uptime. The system tracks its own decision patterns (approval rates, escalation rates, confidence) against historical baselines and alarms on drift within minutes, not weeks. Configuration changes are snapshotted and diffed like code. Degraded judgment is an incident with a pager, not something you discover in a quarterly review.
Run the numbers on your process
Bring one process: the volume, the team size, the error cost. We'll tell you what Deskflow would take over, and what it's worth per year.
No long sales process. We start with your current workflow.