Autonomous Research and Market Intelligence Automation

Three layers. First, synthesis is constrained to retrieved content: the agent only writes claims it can back with a retrieved source, and the final output includes the source URL and retrieval timestamp for every claim. Second, a verification pass runs on key numerical claims and attempts to corroborate with a second source; claims that can't be verified are flagged rather than excluded (you still see them with a 'single-source' label). Third, an audit log captures every source the agent visited, every claim it extracted, and the reasoning chain from retrieval to synthesis, so a reader can trace any sentence in the output back to its evidence. The system is architected to make hallucination visible when it happens rather than prevent it perfectly.

Yes. We connect to any database you've licensed that exposes an API: Bloomberg (requires a Bloomberg Enterprise setup), S&P Capital IQ, Refinitiv, PitchBook, CB Insights, Crunchbase Pro, industry-specific databases (Frost, IBISWorld, Euromonitor), and regulatory data providers. We use your credentials and your license terms apply. Internal data sources (CRM with deal history, research repository, past diligence memos) are indexed into Pinecone with proper access control and included in relevant research queries. A user querying the system sees sources they're authorized to see, same as a native database user. We never resell data or expose your proprietary research outside your organization.

The system is honest about what it does and doesn't do. Data gathering, synthesis, pattern identification, summarization, and variance identification are done by AI. Interpretation, strategic recommendation, investment judgment, and causal reasoning are surfaced as questions for the human analyst to answer using the AI-prepared material. A typical output includes the 'what we found' section generated by the agent and a 'what it means' section structured as prompts for the human ('Given competitor A's pricing model, assess whether TargetCo's premium positioning is defensible'). We design the workflow so AI handles gathering and humans handle the judgment layer, rather than pretending the agent is doing both.

Yes. Each audience gets its own template: an executive brief is a 1-page summary with the top 3-5 findings and a decision recommendation stub; an analyst report is a 10-20 page structured document with full source citations and supporting data; a sales account brief focuses on pain points, recent news, relevant conversation hooks, and known buyer personalities. The same research run generates multiple outputs automatically. We configure templates during setup using examples of your existing output formats so the agent matches your voice. Updating a template (e.g. adding a new section to the IC deck) is a configuration change, not a code change.

Novel research requests outside the configured templates route to a 'custom research' mode where the orchestrator builds a one-off decomposition rather than applying a template. For research on subjects with extremely sparse data (stealth-mode companies, obscure private entities), the agent returns what it found and is explicit about the coverage gap ('no SEC filings, no press coverage in the last 12 months, LinkedIn profile for leadership only'). For research in languages other than English or regions with limited public sources, the agent flags reduced coverage explicitly. It never fakes completeness. Genuinely novel requests that recur become new templates in the quarterly configuration review.

Wrong in this context usually means a stale fact, a wrong attribution, or an incorrect synthesis of multiple sources. Every sentence is traceable to a source, so when the analyst spots an error she can click through to the source and see either (a) the source was outdated, in which case the agent's retrieval was correct but the underlying data was stale, or (b) the agent misinterpreted the source. Both cases feed back: (a) adjusts source ranking to prefer fresher content, (b) adjusts synthesis prompts with the specific misinterpretation. Error rate on high-stakes claims runs 1-2% after month 2 in our deployments, bounded by source quality rather than agent capability.

Every research run writes a complete audit trail: request text, decomposition plan, every source visited with URL and retrieval timestamp, every claim extracted with source linkage, the synthesis reasoning chain, the final output, and any user feedback or edits. The log exports to CSV, Parquet, or direct to your research archive. For regulated financial analysis (equity research, IC materials, underwriting memos) we include a compliance-friendly provenance export showing evidence for every material claim, supporting internal compliance reviews and external audit. Audit logs are retained per your policy (typically 7 years for investment research, shorter for sales briefs).

A focused deployment covering one research type (e.g. competitive briefs or account intelligence) with 2-3 licensed data source integrations runs 8-10 weeks. Weeks 1-2 are discovery: templates, data sources, authentication, distribution channels. Weeks 3-5 build the agent orchestration and source integrations. Weeks 6-7 calibrate against 10-15 sample requests and compare outputs to analyst-produced research for quality. Weeks 8-10 pilot with a small user group, gather feedback, adjust templates and synthesis prompts, then broaden. Adding additional research types (e.g. market sizing as a second pattern after competitive briefs) runs 3-4 weeks each because the platform, synthesis, and templates are reusable.