From broker-bound reporting to talking directly to your enterprise data

Sales and Business Intelligence Agents let anyone talk to the enterprise directly. Instead of filing a request and waiting days for an analyst on top of a BI tool, a leader asks a question in plain language and the agents crawl across Salesforce, ServiceNow, documents and warehouses, correlate the answer, and return it with a dynamic dashboard or a finished deck.

The agents remove the broker between a decision maker and their data. They spin up sub-agents for the sales and business-analyst teams, automate the static, repetitive reporting, and operate strictly inside each person's permissions. The flagship result: enterprise-scale analysis that took 5 to 6 working days now completes in under 2 hours.

Figures are directional and the baseline is set in a short discovery. The under-2-hours result is from an anonymised enterprise meta-analysis across hundreds of presentations and tens of thousands of documents.

In most enterprises, data sits behind brokers. A leader who wants a number files a request; an analyst on top of a BI tool runs it and sends a static report two or three days later. The data is spread across CRM, service management, documents and warehouses that never speak to each other, so the hardest questions, the ones that span branches, reps and assets, take a week of manual effort or never get asked.

• The broker bottleneck: leaders wait 2 to 3 days for an IT or analyst person to run a report on top of a BI tool.
• Static, stale dashboards: reports are built once and age immediately; the next question starts the cycle again.
• Siloed systems: the answer lives across CRM, ITSM, documents and warehouses that are never queried together.
• Analyst toil: skilled analysts spend the week on repetitive, static reporting instead of real insight.
• Scale is prohibitive: a meta-analysis across hundreds of decks and tens of thousands of documents takes 5 to 6 days, so it rarely happens.
• Insight arrives late: by the time the number is in hand, the decision window has often closed.

Our solution is a journey, built from the ground up. First we build the Enterprise Brain. On it we form an Enterprise Data Graph that crawls and stitches every source. On that we layer a Business Ontology, the shared language of metrics, entities and permissions. Only then do the Sales and BI agents go to work on top, each a specialised sub-agent.

Every question runs the same path. A leader asks in plain language; the agents crawl across every connected system, correlate the signal on the business ontology, run the analysis with the numbers checked, and render a direct answer, a dynamic dashboard or a finished deck, always with the source and always inside the asker's permissions.

• No SQL, no ticket: the question is asked in natural language, by the person who needs the answer.
• Crawls everything at once: CRM, ITSM, documents and warehouses are reached together, not one at a time.
• Permission-aware and sourced: every answer respects entitlements and cites where the numbers came from.

Business intelligence is not one task. It is a spread of distinct jobs, each handled by a specialised sub-agent on the same Enterprise Brain, so the sales and analyst teams stay on top of their routines while the boring, static work is automated away.

• Enterprise crawl: reach across Salesforce, ServiceNow and any other system or knowledge base to gather the answer.
• Knowledge-base search: read documents, decks and wikis and pull the relevant facts.
• Query and SQL: turn a plain-language ask into the right query against the systems of record.
• Meta-analysis: correlate across branches, reps and assets at a scale a person cannot match.
• Dynamic dashboards: build a live dashboard on the fly, with no BI backlog and no IT broker.
• Narrative and decks: assemble finished presentations and data stories from the raw enterprise data.

A leading asset management firm needed stakeholder presentations and sharp data points drawn from across the whole business: roughly 800 presentations and 100,000 documents spread across branches, salespeople and assets. By hand, that meta-analysis took 5 to 6 working days. The agents did it in under 2 hours.

The agents crawled every branch, every salesperson and every asset, performed the analysis and the meta-analysis across the full corpus, and assembled the stakeholder deck with the interesting data points surfaced. The volume that made the task impractical for a team is exactly what the agents handle at scale.

The value comes from compressing time at every level. A simple answer drops from days to minutes; a dashboard is built on demand instead of queued; an enterprise meta-analysis goes from 5 to 6 days to under 2 hours. Together they unlock 3 to 5x analyst capacity while leaders act on fresh insight.

Where the value comes from

All figures are directional; the baseline for your estate is measured in a short discovery. The 5-to-6-days-to-under-2-hours result is from an anonymised asset-management meta-analysis.

The honest trajectory: first value at crawling and stitching, then talk-to-data answers, then dynamic dashboards and decks, then scheduled autonomous insight. Access stays governed; a person reviews anything that acts on its own.

Crawl one estate, prove the speed

Give us one or two data sources and a set of leadership questions. We crawl them into the Enterprise Brain, stand up talk-to-data, and show the time-to-answer gain before you grow the data graph source by source.