Before your agents route, prospect, score, or expand an account, Delpha verifies the company, resolves the commercial parent, and returns a decision your agents can safely act on.
One MCP server. Three operations. Evidence on every call.
Agents now act on data they can't verify.
When a human had bad data, the cost was a messy report. When an autonomous agent has bad data, the cost is an action — taken at machine speed, in front of the customer.
Structural hallucination is the AI-native failure mode: an agent confidently misidentifies a global customer as an isolated, low-value lead — and acts on it. Duplicate outreach to existing customers, miscounted pipeline, expansion routed as net-new.
A subsidiary inbound with no history gets routed as net-new SMB — while the parent is mid-negotiation.
A cold agent email lands at a company already in an enterprise deal at the parent level.
One customer counted three times across records — the agent sees three weak accounts, not one powerhouse.
The orchestrator is probabilistic — fluent, and confidently wrong some of the time. Delpha is the part of the stack that isn't an agent: the deterministic primitive it calls when "confidently wrong" is unacceptable.
Plug Delpha into any agent — Claude, GPT, Gemini, LangChain, your own stack — via MCP, or call the REST API directly.
Anchor an ambiguous name, domain, or CRM record to a verified company identity.
Return the commercial parent that matters for GTM — not the legal holdco, not a shell entity.
Verify a proposed parent-child relationship is safe to act on, with confidence and rationale.
The LLM does one job: external identification. Everything around it is deterministic logic on your real CRM data — website-as-identity matching, false-positive prevention, and hierarchy reconciliation. Two examples an LLM structurally can't get right:
Scenario "Bolt" (bolt.com, US fintech) already exists in your CRM. A new record — Viggo — belongs to the European Bolt (bolt.eu). A name-matcher links them and corrupts the tree.
Delpha Resolves Viggo → bolt.eu, finds no website match, refuses the existing record despite the identical name, and creates the correct European parent. Two accurate trees, not one wrong merge.
A real resolution. A lead comes in as "Sephora Brazil" — your agent has to know who actually owns it, and how to treat it, before it acts. Watch the demo, then step through the resolution yourself.
Sees "Sephora Brazil," treats it as a local Brazilian account. Misses that it rolls up to LVMH in Paris — or stops at a family holding shell. Creates a new account; routes Brazil as net-new.
missed: this is LVMHNo — and a better Claude makes us more valuable, not less.
The model reasons over data; we provide the verified, customer-private, audit-logged data and the reconciliation primitives it reasons over. Anthropic and OpenAI have repeatedly signaled they want to be the model layer — not the customer-data layer. The better agents get, the more places they're deployed where being wrong about the parent is costly.
Not the model — it's an incumbent (Salesforce/Informatica, Clay) shipping an agent-ready identity API first. Our answer: we're cross-platform and model-agnostic while they're locked to one stack.
Teams deploying agents reach for what's already on the shelf. None of it was built for pre-action verification in an agent loop.
| Approach | Verifies input | Commercial parent | Evidence + confidence | Agent-ready (MCP) |
|---|---|---|---|---|
| Naive agent + web search | No | Guesses | No | N/A |
| Enrichment APIs | Takes input as given | Legal parent only | No | REST, not agent-shaped |
| Legal hierarchy data (D&B) | No | Legal only | No | Batch files |
| Delpha | Identity anchored first | Commercial parent resolved | On every call | MCP + REST |
Connect the MCP in under five minutes. Run 100 resolutions free. See what verified commercial identity does to your agent's accuracy.
Already using Delpha on Salesforce? delpha.ai is the same resolution engine exposed for agent workflows outside of Salesforce. Learn more on delpha.io.
