How a Research Firm Cut 95% of Report Hours With AI
A research team once spent a month hand-building each 30-page report. Sixteen AI agents now draft, design, and brand-check them under seven human gates — cutting 95% of the hours.
~95%
Cut in human hours per report
The Challenge
A global research and insights business produced flagship thought-leadership reports that were long, data-rich, and fully designed, often 30 or more pages. There are over 200+ reports built every year, and each one takes 24 to 32 calendar days and hundreds of active human hours to move from a structured data file to a finished, on-brand report. The work was slow, hard to scale, and tied up senior people in manual production rather than analysis. It also had to clear strict brand standards and several human review gates at every stage, so any solution had to hold quality and governance, not just speed. The trigger was the need to produce these reports far faster, at consistent quality, without losing the brand rigour or the human checkpoints that protected it.
What They Built
An AI report builder that takes an unstructured Excel data file and turns it into a fully designed, on-brand, editable report, ready for final human sign-off. It runs as a team of 16 specialist agents, each owning one step of the process, from interpreting the data to drafting the narrative, applying the brand and design rules, and laying out the final document. A supervisor coordinates them, so the work moves through the stages in order rather than relying on one general-purpose tool to do everything. Seven human review points sit along the workflow, so the team checks and steers the output at the moments that matter most. Every step is traced and logged, making the output auditable and its quality measurable against agreed standards. The whole system was built and delivered in an eight-week sprint, runs inside the client's own environment, and is now in live production.
The build ran as an eight-week sprint across three phases: design, develop, deploy. In the first two weeks the team went deep on the client's existing report-production process and the brand and design standards a finished report had to meet, working through real past reports and mapping every step from raw data file to published document. Two decisions came out of this: rather than point one general-purpose model at the whole task, they broke the workflow into discrete stages, each handled by a specialist agent; and they placed seven human review points at the moments that most affected quality and brand integrity, rather than a single check at the end. In the develop phase they built the system agent by agent — 16 specialist agents under a supervisor — testing each against real data files and reports as it came online, on a governed, traceable foundation inside the client's own environment so there was no later migration. Finally they ran the full workflow end-to-end on live reports, tuned it against the seven gates, and handed it into production with the team operating it themselves.
AI Role
The system reads a structured data file and turns it into a finished, designed report through a sequence of specialist agents, each doing one job. It interprets the data and pulls out the relevant findings, generates the narrative and written analysis, applies the brand's verbal and visual rules, and lays the content out into a formatted, multi-page document. Alongside that, it checks each output against the codified brand and quality standards, flags anything that fails, and routes the work to a human at seven defined review points for sign-off or correction.
Infrastructure
Runs entirely inside the client's own cloud environment (no external hosting or data migration) • Unstructured Excel data files as the source input • Codified brand, editorial, and design standards as the reference foundation the agents read from
Integration Points
Excel data files ingested into the multi-agent pipeline • Supervisor orchestration routing work across 16 specialist agents in sequence • Seven human-in-the-loop review gates wired into the workflow for sign-off • Full step-level logging and tracing for auditability and quality measurement
Impact
Reduction in active human hours to produce a finished report
Reduction in calendar time, from 24 to 32 days down to a fraction
Full end-to-end cost per report, built and run inside the client's own environment
Implementation Complexity
Best Fit For
Any organisation that produces high-value, structured documents at volume and to a strict brand standard, where the work is currently slow and tied up in skilled human time. Research and insights firms are the clearest fit, but the same pattern applies to anyone shipping data-rich, designed outputs on a repeatable template: thought-leadership and market reports, investor and board documents, regulatory or compliance filings, pitch and proposal decks, and recurring performance reporting. The conditions that make it work: the output follows a repeatable structure; there is a clear, agreed standard for what 'good' looks like; real underlying data exists to build from; and the team wants to keep human judgement in the loop rather than fully automate.
