What is Accrete?
A U.S.–based dual-use artificial-intelligence company that licenses “Expert AI Agents” to government and Fortune 500 customers. Its core IP is a platform that turns tacit human know-how into domain-specific Knowledge Engines.
Frequently Asked Questions
What business problem is Accrete trying to solve?
“Knowledge loss”—i.e., the fact that invaluable expertise lives in the heads of a few specialists and walks out the door every evening. Knowledge Engines preserve that expertise in software so it can scale autonomously.
Where did the idea come from?
CEO Prashant Bhuyan previously built high-frequency-trading systems and realized that transformers could compress contextual knowledge the same way algos arbitrage data in markets. That insight became the Knowledge Engine concept.
How big is Accrete today?
The investor deck shows $9 million ARR in 2024 and an expected $92 million ARR for 2025 (with >$73 million already under contract).
Who uses Accrete?
Customers include DoD, USSOCOM, U.S. Army, U.S. Air Force, and commercial giants in media, consumer goods and IT service-management.
What is a Knowledge Engine (KE)?
A set of AI models that compress domain expertise into a persistent semantic knowledge graph. The KE continuously ingests data, learns from expert feedback, and provides ground-truth “memory” to downstream agents.
How is that different from a large language model (LLM)?
LLMs are static text predictors with no long-term memory. Knowledge Engines maintain a growing graph and therefore reason across silos, refine themselves online, and cite ground-truth kernels—something infinite-context LLMs alone still can’t do.
What is an Expert AI Agent?
It’s an application-layer agent powered by a KE plus task-specific logic. Think of it as a super-specialist that reasons like a veteran analyst but works at machine scale.
How do Agents learn tacit knowledge?
Via “representative examples” and conversational reinforcement. Experts show, correct, and critique; the KE encodes this into its graph so the agent’s future reasoning mirrors the expert’s judgment.
Does Accrete support classified data?
Yes. The platform is on an accelerated path to DoD IL-6 (Top-Secret) compliance by Q3 2025, enabling Agents to reason across both classified and open-source intelligence.
What is Argus for Supply-Chain Influence?
An agent that mines unstructured data to reveal foreign ownership, control or influence (FOCI) across complex vendor networks, surfacing anomalies automatically.
What is Argus for Social-Media Influence?
An OSINT agent that digests multi-platform chatter, maps emerging narratives, and identifies the networks driving them—allowing analysts to counter disinformation early.
How accurate is Argus compared with a general-purpose LLM?
In an investor-deck example asking, “Is Beijing Kuangshi Technology under Chinese government influence?”, ChatGPT gave a vague answer, while Argus produced a precise list of ten entities with “extreme influence” links.
What is Nebula IT Service Management?
A KE-powered agent that predicts high-impact ticket risk, enriches CMDB data, and cuts mean-time-to-resolution (P&G saw 22 → 8 days).
Nebula for Social (Enterprise)—what does it do?
Predicts which hyper-specific narratives will go viral, identifies key influencers, and auto-generates counter-messaging or brand content—reducing analyst “scroll time” by 94 %.
How is a Knowledge Engine deployed?
Agents run in Accrete’s cloud, GovCloud, or on-prem. Data ingestion connectors plug into Snowflake, Databricks, SharePoint, social APIs, and bespoke feeds.
How long does it take to stand up?
Typical pilot: 4–6 weeks to ingest data, encode expert workflows, and hit production accuracy. Government SBIR prototypes completed in under 120 days.
What skills do customers need?
Subject-matter experts to teach the KE, plus standard DevSecOps for integration. No in-house ML engineers are required; Accrete maintains model ops.
What’s the licensing model?
Annual recurring licenses per Agent, tiered by data volume and user seats; platform upsells add new Agents or extend to higher classification levels.
Sample ROI metrics?
- DoD: Identified dozens of illicit Chinese investment networks 3× faster than human teams.
- USAF: 50 % faster engineering cycles, 80 % lower manpower.
Is there a channel strategy?
Yes—Accrete sells direct and via partners (Carahsoft, NTIS, PwC, Publicis, Snowflake, Databricks, New Era IT).
How does the platform handle sensitive data?
All data is stored in customer-controlled enclaves; the KE’s graph is tenant-isolated. FedRAMP Moderate today; IL-6 on the roadmap.
Does Accrete use customer data to train generic models?
No. Each Knowledge Engine is single-tenant; no data or weights are commingled. Customers keep IP, models, and outputs.
What guardrails exist against model hallucination?
Ground-truth kernels in the graph let Agents trace every answer back to a verifiable source document, providing citation-style transparency.
Who is Brian Drake?
Federal CTO; ex-Defense Intelligence Agency Director of AI; created the agency’s first AI strategy and managed a $20 million portfolio before joining Accrete.
What does the Federal CTO do?
Translates Accrete’s commercial AI into secure government offerings, leads IL-6 migration, and represents the firm on national-security AI panels.
What’s launching next?
A self-service Knowledge Engine Platform (Q4 2025) so customers can “build, deploy, and manage an army of Expert AI Agents with a few simple prompts.”
Will Accrete open-source anything?
No public commitments. The competitive moat lies in proprietary graph-building and reinforcement workflows.
Does Accrete plan to support multimodal data?
Yes—current Argus iterations already fuse text, images and structured records; video and sensor modalities are on the 2026 road-map.
How does Accrete compare to Palantir?
Palantir provides a data-integration workbench; Accrete delivers autonomous Agents that already reason over that data. Many customers actually run Accrete Agents on top of Palantir-curated silos.
Versus open-source LangChain Agents?
LangChain orchestrates generic LLM calls; Accrete’s Agents have persistent memory, domain-specific graphs, and validated kernels—crucial for classified or life-or-death scenarios.
“I’m an analyst—will this replace my job?”
No; it optimizes your time. Argus cuts report-writing hours so analysts focus on strategic thinking and policy advice.
“Can I fine-tune the Agent’s tone or output format?”
Yes. Output templates and RAG-style prompt layers are customizable per organization or mission set.
“How frequently do the graphs update?”
Real time to hourly, depending on data-feed SLAs. Continuous learning runs concurrently, so the Agent’s memory grows perpetually.
“What if my data is messy or siloed?”
The Knowledge Engine automatically normalizes schemas and links entities, unifying silos into one semantic graph before reasoning begins.
Can KEs model counterfactual scenarios?
Yes—agents can run “what-if” graph traversals, injecting hypothetical edges and re-evaluating influence or risk.
How many documents can Argus process per week?
Hundreds of millions, per the deck, thanks to distributed ingestion pipelines.
Does the platform support zero-trust architectures?
Agent micro-services run in Kubernetes namespaces with mutual-TLS, aligning to DoD zero-trust reference architecture.
Rapid-Fire Facts (“Did-You-Know”)
- First DoD prototype win: 2019, beating 65 vendors.
- CAGE/UEI codes: 8GAG5 / PKN2JBLNU813.
- GSA schedule: MAS 47QTCA23D00BA.
- Estimated analyst capacity gain: Argus Social cuts workload by 80 %.