Cryptographic provenance, policy-grade governance, and an enterprise context engine — so your CFO, CISO, and General Counsel can finally say yes to agents that touch money, customers, and contracts.
Enterprise pilots open · VPC deployment · SOC 2 roadmap
It's not the model. It's the missing trust infrastructure.
Across Fortune 1000s, agents pass proof-of-concept and stall at the trust boundary. Compliance, security, and finance won't let them touch money, customers, or contractual obligations without three things they've never had: an enforceable policy layer, a verifiable audit trail, and context grounded in the company's actual data and rules.
Without those, autonomy is a liability. With them, autonomy compounds.
"Permission is the unlock. Speed, cost, revenue, and scale all follow." — Jean Gerard de Rubens, Co-Founder & CSO
Skip any one and your agent is a liability. Get all four and it's an employee.
Map your real policies — financial controls, regulatory requirements, contractual obligations, internal SOPs — into machine-enforceable rules.
A multi-tower architecture grounds every decision in your actual data: CRM, contracts, policy docs, service logs, communications.
Sub-second decisions with confidence scoring and human-escalation paths. Calibrated to your risk tolerance, not the model's.
Every decision generates a cryptographic receipt: the policy applied, the context retrieved, the confidence score, the action taken. Tamper-evident. Auditor-ready.
The decision layer sits on your data and policies; the context engine is the substrate inside it — multi-tower retrieval, specialist agents, and receipts on every path.
Data sources your teams already use: Salesforce HubSpot Gmail / Outlook Google Drive OneDrive DocuSign Shopify GitHub Linear & more — ask about your stack during a pilot.
Map your real policies — financial controls, regulatory frameworks, contractual obligations, internal SOPs — into machine-enforceable rules. Agents check before they act, every time. Version-controlled, attributable, reviewable.
The substrate underneath every decision. A multi-tower architecture organizes your CRM, contracts, policy docs, service logs, and communications into semantic domains agents can reason across. Agentic RAG keeps retrieval current. On-the-job learning keeps it sharp.
Sub-second resolution with confidence scoring and human-escalation paths. Agents don't just produce opinions — they produce decisions backed by policy, context, and a confidence interval your team calibrates.
Every decision generates a cryptographic receipt: the policy applied, the context retrieved, the confidence score, the action taken. Tamper-evident. Auditor-ready. Court-defensible.
Purpose-built agents for CRM, contracts, policy documents, service logs, and compliance signals — each an expert in its domain, governed by the same decision layer.
Every confirmed decision, override, and outcome compounds the context engine. Your governance gets sharper the more your agents act.
These aren't dashboards. These are agents executing within your policies, leaving a receipt for every action.
Our proof-of-concept product, in market today: autonomous agents transacting on behalf of buyers and sellers, governed end-to-end by the CogNEXUS decision layer. The thesis, working.
Agents read the contract, the usage data, the engagement signals, and the renewal calendar — and execute renewal motions inside sales policy. The CRO gets pipeline coverage. The CFO gets margin discipline. The GC gets a receipt for every commitment.
Agents evaluate vendor proposals against your SLAs, security policies, and budget envelopes — then make or recommend procurement decisions inside guardrails. CFO sets spend caps. CISO sets the security floor. Agents move at machine speed inside the perimeter.
Service agents resolve tickets, issue credits, escalate disputes — all bounded by policy, all logged with provenance. Customers get speed. Auditors get the record.
Your agents have a budget and a boss.
Your agents operate inside the perimeter you already trust.
Your agents leave a trail your regulators accept.
Frontier model capability crossed the threshold for autonomous action in regulated workflows in the last 18 months. The model isn't the bottleneck anymore.
EU AI Act, US state-level frameworks, and SOX-adjacent guidance are converging on a single demand: provable governance over automated decisions.
Every governed decision sharpens the context engine. Late entrants don't retrofit trust — they earn it from zero, with their first auditor in the room.
This is the substrate underneath every decision the platform makes. Upload any CSV and our engine reads your data, groups columns into semantic towers, trains a predictive model, and returns an interactive insight report — the same context architecture that grounds every governed agent action.
For technical evaluators only. Production decisions run inside your VPC, against your real data, under your policies.
Our LLM agent reads your column headers and sample rows to understand what each field represents — no manual schema mapping required.
Data is automatically organized into 2–5 thematic towers (e.g., Customer Health, Compliance, Revenue) — the same multi-tower architecture behind CogNEXUS.
Box plots, correlation heatmaps, and data-quality diagnostics surface hidden relationships and gaps across your data.
A gradient-boosted model ranks which signals matter most and shows per-tower predictive contribution — revealing where risk actually comes from.
The result is a single self-contained HTML file with interactive Plotly charts — ready to share with your team instantly.
Serial founder with deep expertise in AI systems and ML infrastructure. Previously Solutions Engineering Lead at Arcee AI and Field Engineering Lead at Roboflow. Leads product vision, engineering strategy, and context engine implementation.
25 years across two tenures at Microsoft in enterprise partnerships, plus founder roles at qUbit Corporation (FinTech/blockchain) and pointgrow. MIT Professional Education credentialed in Applied Agentic AI for Organizational Transformation; Prosci change management certified. Bilingual EN/ES. Drives strategy, enterprise relationships, and the seed raise.
Brings 11 years across consulting and multiple startup ventures. As Co-Founder & Chief Vision Officer of CogNEXUS Labs, Adam owns product vision, user-acquisition strategy, and early business development, driving the MoreStore.ai POC as it onboards first clients and prepares to scale.
Senior advisor across AI, text, vision, and marketing. Strategic guidance on positioning and adoption.
We work directly with enterprise teams to onboard your data sources, configure the policy layer to your real rules, and stand up the decision layer around your highest-leverage agent workflows.
Tell us about your use case and we'll follow up within one business day.
