Legal
Cutting Contract Review Time by 74%
74%reduction in initial review time
2 weeksRead the full build →
IN PRODUCTION
The work, in aggregate. Not a pitch deck.
Every number below is the running total across delivered engagements, not a target and not a projection.
100+
Production systems shipped
62,000+
Documents processed (cumulative)
87%
Largest time reduction documented
99.99%
Production uptime
Aggregate across delivered engagements. Methodology available on request.
WHERE PROJECTS FAIL
Most AI projects fail in the sequence, not the model.
The model is rarely the problem. The order of delivery, review, and risk is where most engagements come apart.
| Stage | Typical process | Deep Mist process |
|---|---|---|
| Delivery | 6-18 month timeline | 1-10 day production (14-90 for native apps) |
| Result | Slide decks and recommendations | Working software in your environment |
| Risk | Budget spent before code ships | Production-ready V1 before budget drained |
| Review | Single-team QA | 3-model flagship review + human sign-off |
| Cost | Hourly, open-ended | Fixed price, fixed scope |
| After launch | Handoff document | 30-90 day support |
Delivery
Typical process
6-18 month timeline
Deep Mist process
1-10 day production (14-90 for native apps)Result
Typical process
Slide decks and recommendations
Deep Mist process
Working software in your environmentRisk
Typical process
Budget spent before code ships
Deep Mist process
Production-ready V1 before budget drainedReview
Typical process
Single-team QA
Deep Mist process
3-model flagship review + human sign-offCost
Typical process
Hourly, open-ended
Deep Mist process
Fixed price, fixed scopeAfter launch
Typical process
Handoff document
Deep Mist process
30-90 day supportWhere a typical engagement runs 6 to 18 months, bills hourly, and hands over slide decks and a document, Deep Mist ships working software into your environment in 1 to 10 days (14 to 90 for native apps) at a fixed price, behind a three-model flagship review with human sign-off, and stays on for 30 to 90 days of support.
SELECTED WORK
The builds, end to end.
Anonymized by industry, scale, and role. Each one opens the full build.
RELEASE DISCIPLINE
Three models. Zero blind spots.
Every line that ships is read by three flagship models in parallel, then signed off by a human operator before it reaches your environment.
OUR COMMITMENTS
Five commitments behind every build.
These hold on every engagement, regardless of capability, scale, or industry.
Three-model flagship code review
Every shipped system is reviewed by OpenAI, Anthropic, and Google frontier models in parallel, then signed off by a human operator.
Citation-first outputs
Every system that handles documents or research produces citation-verified outputs. We don't ship systems that hallucinate sources.
Production-only deployment
Every engagement ends with a system running in your environment, handling your real workload. Not a POC. Not a slide.
Human-in-the-loop by default
Approval, release, and rollback paths are owned by your team. AI operators accelerate; humans decide.
Your infrastructure
VPC, on-prem, and private-cloud deployment options for regulated workloads. Your data stays where your compliance team allows.
IN THEIR WORDS
The operators who ran it in production.
Sourced from delivered engagements. Attribution is by role and anonymized organization only.
The system caught clause conflicts our senior associates were missing, and every flag pointed at a real clause we could check. It is not replacing lawyers, it is making them faster.
We got the Monday block back. My team finally has time for analysis instead of data entry, and the numbers stopped breaking when a source system changed.
We expected a proof of concept. They delivered a production system in three weeks that our staff actually wants to use, and it never merges a patient record on its own.
This changed how we evaluate deals. We look at roughly twice as many targets now with the same team, and every line in the brief points back to a source we can check.
Our search finally works. Customers are finding products they did not know we carried, and merchandising still signs off on every batch before it goes live.