Mentiko for Research Teams: Automate Literature Review and Synthesis

Research is supposed to be about insight. In practice, most of it is logistics -- finding sources, cross-referencing claims, reconciling contradictions, and formatting the output into something a decision-maker can act on. Mentiko's event-driven agent chains turn that logistics problem into an automated pipeline.

The research bottleneck

If you run a research team, you already know the numbers. Analysts spend roughly 60% of their time gathering sources, 20% reading and evaluating them, and 20% writing up findings. A thorough literature review takes 3-5 business days. A deep competitive landscape analysis takes longer.

The problem isn't that researchers are slow. The problem is that the work is sequential, manual, and perishable. By the time you've compiled findings from 40 sources, the earliest ones may already be outdated. Cross-referencing between sources is done in spreadsheets or someone's head. Contradictions between sources get caught late -- or not at all.

This is exactly the kind of work that agent pipelines were built for: structured, multi-step, data-intensive tasks where each stage has clear inputs and outputs.

The 4-agent research chain

Mentiko lets you build this as a chain of four specialized agents, each triggered by the completion of the one before it.

Agent 1: SourceGatherer. Takes a {TOPIC} variable and searches across academic databases, news archives, industry reports, and public APIs. It returns a structured list of sources with metadata -- title, publication date, source type, relevance score, and a summary of the key claims. You configure the search scope: which databases to hit, how far back to look, how many sources to collect.

Agent 2: FactChecker. Receives the source list and runs verification passes. It checks claims against other sources in the set, flags contradictions, identifies sources that make claims no other source corroborates, and assigns a confidence score to each source. Sources below your configured confidence threshold get flagged -- not discarded. The distinction matters: low-confidence sources might be emerging research that hasn't been widely cited yet.

Agent 3: Synthesizer. Takes the verified, scored source list and identifies patterns. Where do sources agree? Where do they diverge? What themes emerge across the literature? What's the consensus view, and what's the minority position? The Synthesizer produces a structured analysis that maps the intellectual landscape of your topic rather than just summarizing individual papers.

Agent 4: ReportGenerator. Takes the synthesis output and produces your deliverable. This is configurable through the {FORMAT} variable -- a two-page executive brief for leadership, a full research report with citations for the team, or a structured data export for further analysis. Every claim in the report links back to its source, so reviewers can trace any finding to its origin.

Because Mentiko is event-driven, each agent fires automatically when the previous one completes. You kick off the chain and come back to a finished report.

Research quality gates

The FactChecker agent deserves a closer look because it's where quality control happens.

Every source gets scored on multiple dimensions: publication credibility, recency, corroboration by other sources in the set, and internal consistency. You set the confidence threshold -- say 0.7 out of 1.0. Sources that score above the threshold flow through to the Synthesizer as verified. Sources below the threshold get collected into a separate "flagged" section.

The Synthesizer weighs verified sources higher when identifying consensus, but still includes flagged sources in its analysis with appropriate caveats. This is important for research teams: you don't want the system silently discarding sources. You want it to surface uncertainty so a human can make the call.

The final report includes a confidence breakdown -- how many sources were verified, how many were flagged, and what the overall confidence level of the synthesis is. A research director can look at that breakdown and decide whether the report needs manual review of the flagged items or whether the verified sources alone provide sufficient coverage.

Scheduling for continuous intelligence

A one-time literature review is useful. A continuously updated intelligence feed is transformative.

Mentiko's cron scheduling lets you run research chains on a recurring basis. The same chain, different cadences depending on the use case:

• Weekly for market research and trend tracking. Every Monday morning, your team has a fresh competitive landscape report.
• Daily for competitor intelligence. Track product launches, pricing changes, executive moves, and public statements across your competitive set.
• On-demand for due diligence. When an M&A opportunity hits, trigger a deep research run and have findings in hours instead of days.

Each run uses variables you define: {TOPIC} controls what's being researched, {DEPTH} toggles between surface-level scanning and deep analysis, and {FORMAT} controls the output structure. The same chain handles all three -- you just change the inputs.

Real numbers

Here's what the math looks like for a typical deep literature review:

| | Manual | With Mentiko | |---|---|---| | Time to deliverable | 3-5 days | 2-4 hours | | Analyst hours consumed | 24-40 hrs | 1-2 hrs (review only) | | Cross-source contradiction detection | Inconsistent | Systematic | | Source staleness risk | High | Low (re-run anytime) |

The time reduction comes from parallelism and automation, not shortcuts. The SourceGatherer searches multiple databases simultaneously. The FactChecker runs verification passes in parallel across all sources. The Synthesizer processes the full source set at once rather than reading papers one by one.

Cost: Mentiko's flat-rate pricing starts at $29/month for the platform. The variable cost is your LLM API usage -- roughly $5-15 per deep research run depending on source count and analysis depth. A weekly scheduled run costs approximately $20-60/month in API fees. Compare that to 24-40 hours of analyst time per manual review.

The quality argument is actually stronger than the speed argument. A systematic cross-source comparison catches contradictions that sequential human reading misses. When source #37 contradicts source #4, the FactChecker flags it. A human reader who processed those sources three days apart might not.

Use cases by team type

Academic research. Systematic literature reviews for papers and grant proposals. Meta-analysis preparation -- the SourceGatherer and FactChecker stages produce the structured source evaluation that meta-analyses require. Graduate advisors use scheduled runs to track new publications in their subfield.

Market research. Competitive landscape analysis with recurring updates. Trend analysis across industry publications, earnings calls, and analyst reports. The Synthesizer's pattern detection is particularly strong here -- it identifies emerging themes before they become obvious in any single source.

Due diligence. M&A target research, vendor evaluation, and partner assessment. The on-demand trigger lets deal teams launch a research chain the moment an opportunity surfaces. The confidence scoring gives decision-makers a clear picture of how well-supported each finding is.

Policy and regulatory. Regulatory change tracking across jurisdictions. Impact assessment for proposed legislation. The scheduling feature keeps policy teams current without dedicating analysts to monitoring duties.

Getting started

The research chain template is available in the Mentiko marketplace. Install it, configure your LLM API keys (they never leave your instance), set your topic variables, and run it. The first run will take longer as the SourceGatherer builds your initial source set. Subsequent scheduled runs are faster because the system diffs against previous results.

Your API keys, your sources, your reports -- all on your dedicated instance. Nothing shared, nothing stored on our infrastructure.

Join the waitlist to get your own Mentiko instance and start building research pipelines.