Case study 03 / Client research platform
5,000+ briefs yearly.
65,000 hours saved.
A custom multi-agent system decomposes company research, verifies every claim against a source, reconciles the findings, and produces a client-ready document in minutes.
5,000+
Company researches completed per year
65,000+
Human hours saved at client volume
$600K+
Equivalent salary capacity avoided
7h → <10m
End-to-end research turnaround
The research bottleneck
Cut seven hours
from every brief.
Client teams needed deep company research before they could advise, sell, or prepare an engagement. A single useful brief required hours of navigating websites, checking leadership, products, markets, hiring, customers, and competitive signals.
At more than 5,000 researches per year, the volume implied capacity equivalent to roughly fourteen full-time research assistants. Scaling the manual process would have added more handoffs, more inconsistency, and more opportunity for unsupported claims.
The output also had to be usable. A faster wall of generated prose was not the goal. The system needed facts tied to sources, consistent sections, explicit gaps, and formatting that could move directly to a client.
The system
Run nine agents.
Produce one brief.
Each agent owns a narrow research question. A paired verification step checks its evidence. Only then does a normalization layer merge, cite, structure, and format the output.
Research run / live architecture
One request, nine parallel investigations
The grounding contract
Source every claim.
Reject unsupported facts.
The system did not allow plausible language to stand in for evidence. Every material claim had to resolve to something the company actually published. If the source was missing, the output said so.
The time equation
Turn seven hours
into ten minutes.
01 / Before
~7 hours
Manual browsing, synthesis, fact checking, and formatting for every company.
02 / System
~5 min
Nine agents research in parallel, verify evidence, normalize findings, and format the document.
03 / Human review
~5 min
Inspect sources, resolve flagged gaps, and approve the client-ready brief.
5,000+
researches completed every year
65,000+
human hours saved at client volume
$600K+
in equivalent salary capacity avoided
Beyond research
Monitor at scale.
Escalate only ambiguity.
The broader product also monitored hiring signals across hundreds of thousands of companies by connecting directly to underlying job-portal APIs. Clear qualification cases were handled by semantic logic. Only ambiguous, low-confidence records escalated to AI. The same principle governed the entire product: deterministic systems for volume, agents where judgment earned their cost.
Large-scale collection
Daily monitoring across hundreds of thousands of companies.
Confidence-gated AI
Simple cases stay cheap and fast. Ambiguity gets intelligence.
Product ownership
Customer discovery, UX, roadmap, Python, AI, sales, and support.
The outcome
Seven hours became
ten minutes.
More throughput, consistent structure, every claim grounded, and human attention reserved for judgment rather than collection.
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