The unified context layer for agentic AI
One governed source of context for every agent — and a complete audit trail on every action. So US financial institutions can move agents from pilot to production without losing transparency, control, or human oversight.
Trust, by construction — not bolted on.
Built for US financial services
For the teams that carry the risk — lending, servicing, claims, and compliance. Start on one workflow; verify it on your own data before anything scales.
Customer logos will appear here as design partners go live. [PLACEHOLDER: logos — source needed]
The problem
The pilot works and the demo dazzles. Then the agent meets a real regulated workflow — fragmented data, unexplainable decisions, no record of who approved what — and it never ships. The gap isn't the model; it's that nothing underneath it was built for trust at scale.
of companies report no tangible EBIT impact from generative AI.
McKinsey · The State of AI
of executives describe their gen-AI rollouts as “mature.”
McKinsey · The State of AI
are scaling AI agents in any single function — most are stuck experimenting.
McKinsey · State of AI 2025
The technology to build agents is here. The system to trust them isn’t — until you put one underneath.
The shift
Wire each agent to each system and bolt an audit log on afterward, and you get something brittle, invisible, and impossible to govern. BlackGrid inverts it: one governed context layer in the middle — agents draw context from it, act through it, and are recorded by it. Trust and lineage come from the architecture, not from hope.
Before
Point-to-point. Brittle. Unauditable.
After
One layer. Governed. Traced end-to-end.
How it works
BlackGrid sits between your systems and your agents and does three things every time an agent acts.
01 · GROUND
BlackGrid unifies the knowledge scattered across your core systems and serves each agent only what it's cleared to see — current, complete, and tied to its source.
IBM: <1% of enterprise data lives in today's models — context has to come from somewhere governed.
02 · VERIFY
Answers are anchored to the sources they came from, so an agent's response can be traced, checked, and defended — not taken on faith.
McKinsey: inaccuracy is the #1 AI risk teams actually hit; explainability is the one they under-manage.
03 · GOVERN
Define checkpoints on high-stakes or irreversible actions, and BlackGrid records the full lineage — what was known, what acted, who approved — as a by-product of running.
McKinsey: human-in-the-loop is the top practice of AI high performers — 65% vs 23%.
What you get
One governed source of truth for every agent and every human.
Connect your core systems, claims, CRM, and documents once. BlackGrid resolves them into one permissioned context layer — so every agent and every person works from the same current picture, scoped to exactly what they're cleared to see.
BCG: fragmented, siloed data is why insurers stall on AI — the fix is to unify data and workflows.
Every answer grounded. Every decision explainable. The black box, opened up.
BlackGrid grounds every agent response in the specific sources behind it and keeps that link intact — so any output reads back to its evidence. When something's wrong, you see why, not just that. The black box becomes a black grid you can audit.
Geneva Association: accuracy and transparency are customers' top priorities for AI in financial services.
The human stays in the loop. The record stays complete.
Put human checkpoints on the actions that carry real consequence, guardrails on the rest, and get a complete, exportable lineage of every action — ready for your risk committee, your auditor, and your examiner. Oversight you configure; accountability you can prove.
Built for US model-risk & AI-governance expectations — documented oversight, explainability, and a human in the loop (e.g., SR 11-7, the NAIC AI Model Bulletin, the NIST AI RMF). [PLACEHOLDER: confirm regulatory citations with US counsel]
Why BlackGrid
Every homepage in the category says “auditable” and “human-in-the-loop.” The difference is where it lives: for most tools trust is bolted on after the fact — a log nobody reads. For BlackGrid it's a property of the layer everything runs on.
| Point agents + bolt-on audit | BlackGrid context layer |
|---|---|
| Each agent wired to each system | One governed layer between systems and agents |
| Context fragmented; agents guess | Shared, permissioned, current context |
| Audit log added afterward, partial | Full lineage as a by-product of running |
| “Trust us” — explainability optional | Every output traceable to its source |
| Oversight inconsistent, per-tool | Configurable human checkpoints, enforced |
| Knowledge search (find a doc) | Knowledge governance (act and account) |
Enterprise search tools (Glean and the like) help a person find a document. BlackGrid governs the context an agent acts on — and lets you prove it should have.
Why it matters
When agents can be trusted in production, the value starts to land. The numbers below are the market's, not ours — the case for moving, and for moving carefully.
of recent US banking AI research came from just five banks — the leaders are compounding their lead.
Evident Insights
of firms give AI governance CEO-level oversight — the factor most correlated with bottom-line impact.
McKinsey
projected financial-services AI spend by 2027, up from $35B in 2023.
World Economic Forum
of public software earnings calls now mention AI — the pressure is on the board's desk.
ICONIQ (enterprise SaaS)
Figures describe the market, sourced as cited (the $97B spend figure is reported by the WEF via Tenity). BlackGrid's own results will be published as design partners report them. [PLACEHOLDER: BlackGrid customer outcomes — source needed]
In their words
“To realise gen AI's full potential, insurers must embed strong governance, human oversight, and ethical safeguards in their operations, aligning technological innovation with customer trust.”— Geneva Association, Gen AI in the Insurance Customer Journey (2025). Industry source shown while design-partner results stay under NDA. [PLACEHOLDER: named customer testimonial — source needed]
HOW WE START
No leap of faith required. We start on one real workflow, with results you verify in your own environment before anything scales.
Getting started
We work hands-on with a small number of US financial institutions to put trustworthy agents into real operations — scoped to one workflow, with a clear path from pilot to production.
Questions
Integration & deployment
Trust, data & control
Book a demo
Bring a real workflow — loan servicing, underwriting, a claims queue, a KYC review. We'll show you what it looks like with one governed context layer underneath: grounded, overseen, and auditable end to end.
A Trust Architecture brief is on the way. [PLACEHOLDER asset]