01What’s written down is scattered.
The facts live in dozens of your systems that don’t talk. Nobody sees the whole picture.
AI Context Platform
Digital workers, grounded in your organization’s context
Expert AI agents use connected information to do specific jobs. Start with a workflow your team can inspect and evaluate.
The problem
The smartest AI tools still don’t know your business.
Knowledge capture, understanding context, and continuous learning are impossible given today’s data, tools, and rate of change.
01
02How things really get done lives in people’s heads.
The unwritten rules, the patterns, the “who to ask” — new hires take years to absorb them.
03What your best people just know walks out the door.
The hardest calls depend on a few experts’ judgment. When they go, it goes with them.
The work piles up, risk increases, and the best people spend their day on triage instead of valuable work.
42% of what an employee knows about doing their own job is never shared with a colleague.
Panopto, “Workplace Knowledge and Productivity Report,” 2018
The gap
Why this gap has been hard to close.
Every tool on the stack was built for a different job: answering questions, finding documents, modeling data, hiring people. None was built to connect everyone and everything.
Generic AI assistants
Built to answer anything. Real work needs an answer solid enough to act on.
Search & retrieval (RAG)
Built to find what’s written down. The hardest calls aren’t in any document.
Even with retrieved context, 43.1% of responses in the RAGTruth corpus contained hallucinations. — Niu et al., “RAGTruth,” ACL 2024
Hand-modeled data platforms
Built for a business that holds still. The business changes faster than consultants can re-draw the map.
People and outsourcing
The only way to buy judgment. More output means more people, and the expertise never becomes the company’s own.
The solution
The work, finished, while keeping your most valuable assets in-house.
Expert agents take the whole task: research, analysis, the draft, the recommendation. The knowledge they build stays with you, not with a model vendor. Named people still own the calls that need an owner.
Underneath the agents is the Accrete Knowledge Engine. Pointed at your environment, it builds a living map of how your organization actually works, and keeps it current.
Knowledge Engine Platform
Ask for an outcome. Get the finished work.
The Knowledge Engine brings the conversation, connected work, and finished artifact into one visible path.
KEP
Revenue operations review
Review Q2 pipeline health by region and identify the biggest revenue risk.
I connected pipeline history, stage movement, activity, and regional targets. I’m preparing the risk brief now.
Q2 pipeline risk briefReport
58%
lower token cost
when using Knowledge Engine
2x
more tasks
vs. frontier models
32%
more accurate with open source
vs frontier model
19 of 34
tacit-knowledge facts captured
vs. RAG’s 1
Source: “Knowledge Engine Platform for Long-Horizon Professional Agents” and “Context Graphs for Recovering Tacit Organizational Knowledge”, Accrete AI, August 2026.
How it works
How knowledge capture turns to judgment
Step 01
Ground it.
Accrete points the Knowledge Engine at your environment — tickets, logs, contracts, the work people do every day. The knowledge map is standing and queryable in 2–4 weeks. No year-long integration.
Step 02
Put it to work.
Expert agents take on one painful workflow, benchmarked against your organization’s own ground truth, with the team in the loop.
Step 03
Scale it.
Proven workflows expand across your organization. Each one starts from the grounding the last one built.
Proof
Client results
Case 01
A leading entertainment company
~92%
time savings on artist analytics (2 hours → ~5 minutes)
45
weekly reports automated
87%
faster brand-risk detection
30+ years
of content unified
Case 02
A private investment fund
2 weeks → 6 min
investor reporting turnaround
500+
investors reported on simultaneously
92%
accuracy, with zero hallucinations in reports
Case 03
A leading global P&C insurer
200M+
records reconciled into one graph
1–3 months
pilot to value (customer-deployment timeline)
After an enterprise-search deployment had already failed.
Where to start
Start where it hurts most.
Data Management
Data spread across systems that don’t talk? → One living map of how the business actually runs.
See Data Management →
Marketing
Insight buried across channels and agencies? → Brand risk and performance, surfaced from everything at once.
See Marketing →
IT Ops
Buried in triage? → Incidents resolved with the full story behind them.
See IT Ops →
Security / SecOps
Drowning in tools and alerts? → One pane of glass; act on what matters.
See Security →RevOps
Forecasts built on numbers nobody trusts? → Pipeline reality, traceable to the source.
See RevOps →Sales
Reps researching instead of selling? → Account context assembled before every call.
See Sales →Customer Support
Answering the same question for the tenth time? → Answers that carry the full history.
See Support →
Finance
Closing the books by hand? → Reporting that reconciles itself, and stands up to an auditor.
See Finance →