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AI Agents for Research

Research takes time because reading takes time. Literature reviews, competitive analysis, market sizing. Your team reads hundreds of sources to produce a single brief. AI agents do the reading and synthesis so your researchers focus on generating original insights.

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AI Agents for Research

The Problem

A typical research team at a strategy firm, pharma company, or investment fund produces 8 to 12 deep briefs a quarter. A single literature review pulls 120 to 300 sources across PubMed, arXiv, Google Scholar, industry reports, patents, SEC filings, and company 10-Ks. A senior researcher spends 60 to 80 hours reading, tagging, and synthesizing, then another 20 hours writing the brief. Competitive intelligence decays fast: a compiled report about a competitor's pipeline is stale within two weeks because a new clinical trial registered or a patent filed in the interim. Market sizing means reconciling conflicting numbers from IDC, Gartner, Frost & Sullivan, and public earnings data, which takes days and still produces a wide range. Internal knowledge is scattered across 40 Confluence spaces, a SharePoint drive, and 18 people's Notion workspaces, so each new project starts from scratch. Your smartest people spend 70% of their time gathering information and 30% thinking about what it means. The economic asymmetry is the opposite of what you'd want.

How AI Agents Solve It

A Claude Sonnet 4.5 agent with retrieval tools over PubMed, arXiv, Google Scholar, SEC EDGAR, patent databases (USPTO and EPO), a Perplexity API wrapper for live web, and a Pinecone index over your internal Confluence, Notion, and SharePoint. The researcher specifies a question and scope (for example, a 4-week narrative literature review on CAR-T safety profiles in solid tumors, Q4 2024 updates). The agent builds a search strategy, pulls 200+ candidate sources, ranks by relevance using semantic similarity plus citation weight, extracts key findings per source with exact quotes and citations, and synthesizes cross-source patterns into a structured brief: executive summary, major themes, contradictions flagged, open questions, source-by-source appendix. Every claim links back to its source with a clickable citation. The researcher validates and adds interpretation. For ongoing competitive intelligence, the agent monitors specified sources daily and posts updates to Slack when anything material appears.

How It Works

1

Define and Scope

The researcher specifies the question, the relevant sources, any inclusion or exclusion criteria, and the output format. The agent drafts a search strategy covering academic databases (PubMed, arXiv, Google Scholar, SSRN, IEEE Xplore as relevant), industry sources (SEC EDGAR, patent databases, Bloomberg or Pitchbook if licensed), and your internal knowledge base indexed in Pinecone. It proposes search terms, date ranges, and source weighting before executing. The researcher reviews and approves or adjusts. Failure modes: if a requested source isn't accessible (license expired, API down), the agent flags it upfront and offers alternatives rather than silently excluding.

2

Gather and Synthesize

The agent executes the search strategy, pulls candidate sources (typically 150 to 500 depending on scope), and ranks them by relevance using semantic similarity to the research question, citation count, recency, and source authority. For each high-ranked source it extracts key findings as direct quotes with page or section references, tags them by theme, and surfaces cross-source patterns. It explicitly flags contradictions: when two sources make incompatible claims, both get cited side by side with methodology notes so the researcher can judge which is more credible. Failure modes: when source quality is uneven (one strong paper contradicting many weak ones), the agent reports the distribution rather than majority-voting.

3

Report and Monitor

The agent produces a structured brief in the format you specify: executive summary, major themes with cross-source synthesis, contradictions explicitly called out, methodology section describing the search strategy and limitations, source-by-source appendix with citations. Every claim in the brief links to its source and the supporting quote. For ongoing topics, the agent sets up monitoring on the search strategy and posts Slack updates when new relevant publications appear, ranked by materiality. Failure modes: if new sources substantially change a prior conclusion, the agent flags this as a revision needed rather than silently updating a past brief.

What You Get

Literature reviews in hours

A review that takes a senior researcher 3 to 4 weeks end to end produces a structured first draft in 3 to 6 hours. The researcher then validates, adds interpretation, and refines the framing. Total elapsed time on a comprehensive review drops from 160 researcher-hours to 25 to 40, with the higher-value hours going to thinking rather than reading. One pharma client's medical affairs team now produces 3x the volume of briefs with the same headcount.

Always-current competitive intel

The agent monitors competitor activity daily across press releases, SEC filings, patent grants, clinical trial registries, leadership changes on LinkedIn, and news feeds. Material updates post to Slack within hours of publication with context on why they matter. One industrials client caught a competitor's patent filing for a product they were about to launch, 6 weeks before the trade press picked it up, and adjusted positioning in response.

Cross-source synthesis

The agent connects findings across academic papers, industry reports, patent filings, and earnings commentary in ways that are hard to see when reading sources one at a time. It spots consistent patterns and flags contradictions. A researcher looking at semiconductor packaging trends now gets a synthesis across IEEE papers, Morgan Stanley reports, TSMC earnings transcripts, and patent filings in one brief instead of maintaining four parallel mental models.

Full source traceability

Every claim in every brief links back to the original source with the supporting quote and page reference. Your researchers can verify any finding with one click. This matters for regulatory submissions (pharma), due diligence memos (PE and VC), and any context where a senior reviewer needs to audit the reasoning. One Series B biotech passed an FDA information request in 2 days instead of 3 weeks because every citation was structured and traceable.

10x
faster research brief production
500+
sources analyzed per query
2-5 wks
to production deployment

Related Solutions

AI Agent DevelopmentView →
Multimodal RAG SystemsView →
AI Knowledge BaseView →

Related Use Cases

Research AutomationView →
Knowledge Base SearchView →

Implementation

Timeline

3-phase, 4-6 weeks total: Week 1 discovery and integration plan, Weeks 2-4 build and evals, Weeks 5-6 shadow mode and cutover.

Human in the Loop

Researchers review every brief before it leaves the team. The agent's drafts are never published externally without human validation and sign-off. For regulatory or legal contexts, a second researcher reviews independently. Contradictions flagged by the agent require a researcher to explicitly resolve them in the published version. Quantitative claims (market size, efficacy rates) require researcher validation of the methodology. Monitoring alerts fire to Slack for researcher triage, not direct executive distribution. All review expectations are configurable per research area and reviewed quarterly with research leadership.

Stack

Claude Sonnet 4.5PineconeTemporalPostgresPerplexity API

Integrations

ConfluenceNotionSharePointinternal wikisPerplexity API

Frequently Asked Questions

What sources can the agent access?+
Public sources: PubMed, arXiv, Google Scholar, SSRN, IEEE Xplore, ACM Digital Library, SEC EDGAR, USPTO, EPO, ClinicalTrials.gov, FDA guidance pages, court records through CourtListener, and curated news feeds. Commercial sources through your license: Bloomberg Terminal, Pitchbook, CB Insights, Crunchbase, Factiva, LexisNexis, S&P Capital IQ, Morningstar, Gartner, Forrester, IDC, Frost & Sullivan. Internal sources: your Confluence spaces, Notion workspaces, SharePoint, Google Drive, and any shared drive indexed at deployment. We also support Perplexity API for live web where appropriate. Adding a new source takes 1 to 3 days if it has an API, 1 to 2 weeks if we need to build a scraper with ToS compliance.
How does it handle conflicting information?+
The agent flags contradictions explicitly in every brief. When two sources make incompatible claims, both get cited side by side with methodology notes: sample size, study design, date, source authority. The agent does not majority-vote and pick a winner. It presents the disagreement and lets your researcher decide which claim is more credible based on the evidence. For quantitative data (market size estimates, efficacy rates), it shows the range across sources explicitly and often represents the distribution visually. Contradictions flagged by the agent are a feature, not a failure. Making the disagreement visible is more useful than producing a confident but wrong synthesis.
Can it do quantitative market sizing?+
Yes. The agent pulls market size estimates from public and licensed sources, applies your assumptions (geographic scope, segment definition, time horizon), and produces a bottom-up or top-down estimate with the methodology documented. It shows the range of estimates across sources, flags which are vendor-sponsored or self-reported, and makes the confidence interval explicit. For bottom-up sizing, it can pull unit economics from public filings, apply your conversion assumptions, and aggregate. Your researcher reviews the methodology and the source selection before any number lands in a memo. The agent doesn't hide the uncertainty behind a point estimate.
Does it work with proprietary research databases?+
Yes, if the database has an API or supports authenticated access. Production integrations exist for Bloomberg Terminal (BLPAPI), Pitchbook, CB Insights, Crunchbase Enterprise, Factiva, LexisNexis, S&P Capital IQ, Morningstar, Gartner, Forrester, IDC, PACER, and most major commercial research platforms. Your license terms apply: the agent accesses only what your subscription covers and logs usage for audit. For platforms without APIs, we can work with export workflows if your license allows, though this slows retrieval. Adding a new licensed source typically takes 1 to 2 weeks depending on authentication complexity.
What happens when the agent isn't sure? Does it just guess?+
No. The agent attaches confidence to each claim in a brief based on source quality, cross-source agreement, and retrieval certainty. Low-confidence claims are flagged in the draft and explicitly called out for researcher verification. For questions where the literature is genuinely ambiguous (mechanism of action still under investigation, market size estimates with wide variance), the agent reports the ambiguity rather than fabricating a clean answer. If asked a question with insufficient evidence in the accessible sources, it returns what the evidence does show and names what's missing, rather than inventing claims. Hallucinating citations in research work would destroy trust, so guardrails are strict.
Who owns the decision if the agent gets it wrong?+
The reviewing researcher who validates and signs the brief. The agent produces a structured draft with citations. Human researchers validate the citations, check for missed sources, add interpretation, and take responsibility for the final output. Every brief carries the agent's draft version, the reviewer's edits, and the final published version in version control. If a brief turns out to be wrong, the audit trail shows exactly what the agent provided, what the reviewer changed, and where the error was introduced. For regulatory submissions in pharma or legal memos in litigation, a second researcher often reviews independently. The agent is a force multiplier, not a source of final authority.
How long until we see ROI?+
Most research teams see payback within 4 to 6 months. Primary drivers: researcher time reclaimed on search and extraction (typically 70% of a senior researcher's time before automation), increased brief throughput (most teams produce 2 to 4x as many briefs with the same headcount), and faster decision cycles (competitive intelligence that's current matters more than intelligence that's thorough but stale). A pharma medical affairs team with 6 researchers typically sees $900K to $1.4M in annualized capacity gain against implementation cost of $180K to $250K. The bigger value is often qualitative: your best researchers finally do strategic work instead of grinding through PubMed abstracts.
Can we audit every decision the agent made?+
Yes. Every search query, every source pulled, every extraction, every synthesis step, and every claim in a brief links to its source with a quote. Your researchers can retrace the agent's reasoning from the final brief back to the original publications. Standard reports include sources accessed (for license compliance), queries run, briefs produced, and override frequency from reviewers. For regulated contexts (FDA submissions, NICE HTA submissions, SEC filings), the audit trail satisfies the reproducibility requirements of most frameworks. Briefs version in Git. Source access logs retain for 13 months. If your general counsel or regulator asks how a specific claim in a published brief was developed, you can produce the full chain in minutes.

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