Where we deploy.

Revenue agents that operate inside Salesforce, HubSpot, NetSuite, Outreach, and Stripe. Lead enrichment, account research, deal qualification, renewal forecasting, quote-to-cash automation. We deploy operators who ran revenue at high-growth companies before they automated it, which is why the systems we build hold up under quota pressure.

Typical engagements

• Inbound qualification and routing agents that compress lead-to-meeting from days to minutes.
• Account research and outbound briefing agents that replace the manual SDR research layer.
• Forecasting and deal-hygiene agents that surface slipping deals before the QBR.
• Renewal and expansion agents tied to product usage signals.

What you get at the end

An agent your revenue ops and engineering teams own. Documented prompts, versioned in your repo. Eval suites your team runs. A model-agnostic architecture so when the underlying LLM changes, you swap, not rebuild.

Knowledge agents over Box, SharePoint, Google Drive, Notion, Confluence, and the long tail of internal wikis nobody maintains. Document agents that ingest contracts, statements of work, policies, and regulatory filings, and answer the questions your senior people are tired of answering.

Typical engagements

• Contract review and obligation extraction agents for legal and procurement.
• Policy Q&A agents for HR, IT, and compliance with citation-backed answers.
• Onboarding agents that turn fragmented documentation into a working playbook for new hires.
• Internal research agents that compose memos from the corpus of past memos.

What you get at the end

Production-grade retrieval architecture. Permission-aware. Auditable. Built on top of the systems you already pay for, not a parallel knowledge base that goes stale.

PE-backed platforms with active roll-up theses use clus.ai to compress integration timelines. Agents that reconcile two charts of accounts, two CRMs, two ticketing systems, and the inevitable Excel artifacts that nobody wants to touch. We've placed operators who have integrated more than 30 companies between them.

Typical engagements

• Day-one financial close acceleration with reconciliation agents.
• Customer data unification across two CRMs without forcing a platform migration.
• Policy and procedure harmonization agents that map the acquired entity onto the platform's standards.
• Knowledge transfer agents that capture institutional memory from departing leadership.

What you get at the end

An integration playbook codified into agents your platform team reuses on the next acquisition. Each deal compounds. The tenth integration runs faster than the first because the agents are already trained.

Salesforce launched an FDE Partner Network. ElevenLabs has FDEs. OpenAI and Anthropic hire them directly. The category is real and your buyers know it. The problem is that platform-tied FDEs only work on platform-tied problems, and the talent market for independent FDEs is thin and expensive.

clus.ai is the network that solves both. Pods, not individuals. Senior, not staff-aug. Independent, not vendor-tied.

Typical engagements

• 3 to 8 person pods embedded for 10 to 80 days, scoped to a specific production milestone.
• Multi-platform deployments where no single vendor's FDE network spans the stack.
• Bridge engagements while the client builds an internal FDE function.
• Surge capacity for in-house teams shipping against a board-level deadline.

What you get at the end

A working agent. Documented architecture. Trained internal team. And a relationship with the network so the next engagement starts on day one, not week three.