Research that ships as a system—not a one-off answer.
Orchestrated agents, schema-bound extraction, long-form outputs, and the operational layer—watchlists, automations, Agent Trust, attribution—so your team can run investigations in production, not just in a chat window.
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Active run
Competitive landscape: EU fintech compliance & GTM motion
Research depth
3 levels
Light · Standard · Aggressive
Output surfaces
2+
Markdown + PDF export
Governance rails
4+
Trust, queue, alerts, attribution
In-product surfaces
The same window chrome appears across runs, watchlists, queues, and webhooks—operators learn one visual language while the backend handles depth, extraction, and governance.
Run complete
Account brief: Vertico · enterprise RFP response pack
Active run
Healthcare policy: CMS updates & payer reactions (US)
Queued
Vendor deep-dive: API reliability & incident history
Why teams switch
Chat tools answer prompts. Radlect runs research programs.
General-purpose assistants are invaluable for drafting—but they rarely encode how your org decides, what shape evidence must take, or who may press “send” on an automation. Radlect Deep Research is built for that gap: repeatable campaigns, structured outputs, monitors, approvals, and attribution in one place.
Alert fired
3 rules matched · severity ≥ MEDIUM
SEND_WEBHOOK · Partner risk summary
Watchlist: Healthcare vertical · Run #4821
Use cases
Depth where it matters—patterns you can reuse everywhere else
Below are representative programs customers run on Radlect. Your schema and campaign names will differ—the architecture stays the same: define once, execute many times, prove impact when asked.
Payload preview
{
"runId": "run_9f3…",
"status": "READY",
"campaign": "Q3 diligence",
"extraction": { "risk_score": 0.42 }
}Linked deep research
Audit: MCP tool-use trace · vendor evaluation run
Campaign defaults
Competitive & market intelligence
- Track positioning moves, pricing experiments, and narrative shifts across regions.
- Bundle evidence into a single markdown narrative plus schema fields for scoring and rollups.
- Refresh on a cadence with watchlists instead of re-writing the same mega-prompt each quarter.
Deal desk & account research
- Standardize how reps and solution engineers answer complex technical or regulatory questions.
- Reuse campaign defaults so tone, depth, and citation style stay consistent across territories.
- Export PDFs for RFP attachments or internal deal review without rebuilding the doc by hand.
Policy, risk & compliance awareness
- Monitor regulatory summaries, guidance changes, and sector-specific interpretations.
- Structure extraction so legal and GRC partners can diff what changed between runs.
- Pair high-stakes automations with the queue so outbound actions get explicit approval.
Product & technical discovery
- Synthesize vendor docs, integration constraints, and ecosystem chatter into one run.
- Escalate intensity when the question is novel; stay light when you only need orientation.
- Feed structured fields into roadmaps, PRDs, or internal wikis without a second parsing pass.
Corporate development & strategy
- Build comparable views across targets with repeatable schema sections.
- Archive runs so leadership can reopen the evidence trail months later.
- Link attribution when outcomes should map back to a specific research cycle.
Enablement at scale
- Turn one strong research pattern into a campaign every teammate can run with the same guardrails.
- Ship updates to Slack or webhooks when watchlists detect a material change.
- Reduce one-off escalations to subject-matter experts by publishing canonical research packs.
How teams use it
From brief to evidence—in one loop
Each step below maps to concrete surfaces in Radlect: campaigns, Deep Research, the automation queue, watchlists, attribution, and Agent Trust—not abstract consulting language.
Define once
Campaign + schema set how research runs: tone, depth, extraction shape, default webhooks, and when human approval is required before side effects.
Run or schedule
Launch Deep Research for a focused investigation, re-run with prior context, or attach cadence via watchlists so the world does not move faster than your brief.
Review & ship
Scan stage progress, read the long-form narrative, validate structured fields against your schema, export PDF, and clear the automation queue when actions are sensitive.
Trace impact
Use attribution to connect downstream events to research outputs, and Agent Trust when you need defensible review of agent behavior—not a screenshot of a chat.
Compare
Same LLM era—different operating model
This is a simplified framing chart: many agencies deliver excellent bespoke work, and chat tools are unbeatable for improvisation. Radlect targets the middle—teams that need repeatability, structure, and governance without a full-time bench for every question.
Icons indicate native support or first-class workflows. “Varies” means you might bolt it on externally.
Typical chat thread
User: "Summarize everything about our competitors in Germany…"
Long prose reply · no schema · no queue · no recurring cadence—great for one-offs, fragile for programs.
Radlect workspace slice
Campaign defaults
Platform
What we mean by “system”
A single impressive answer is not a platform. Radlect Deep Research is the loop: define how research should behave once, run it on demand or on a schedule, approve automations when stakes are high, and trace outcomes when leadership asks what moved the needle.
- Multi-agent runs with visible stage progress
- Structured extraction aligned to your schema
- Watchlists with flexible condition logic
- Automation approvals, retries, and audit notes
- Agent Trust and deep-research-linked audits
- Credits that reflect real agent work—not flat tax
Linked deep research
Audit: MCP tool-use trace · vendor evaluation run
Architecture
Modes and lifecycle—without hand-wavy diagrams
Operators care how work moves: which agents run, when extraction locks, and what happens before a webhook fires. Here is the spine most customers internalize in the first week.
Trace preview
Orchestrated run · multi-agent path visible in UI
Orchestrated runs
Default for messy, multi-source questions
- Multiple agents can specialize—retrieval, synthesis, verification—before extraction and writing.
- Better when sources conflict, when freshness matters, or when you need depth over speed.
- Intensity still controls how hard the stack pushes before it stops.
Direct mode
When you want a straighter line from prompt to output
- Fewer moving parts for narrower questions with stable sources.
- Useful for templated briefs where the campaign already narrowed the world model.
- Same schema and webhook story—only the internal routing changes.
Intake
Campaign supplies context, instructions, schema, and intensity.
Agents
Search and reasoning stages run with visible progress in the UI.
Extraction
Structured fields populate from your schema; errors surface clearly.
Document
Long-form markdown is assembled for humans; optional PDF export.
Hooks
Webhooks fire on completion; watchlists may trigger alerts or automations.
Governance
Agent Trust and the automation queue capture review where required.
Research intensity
Three depth levels—pick the cost curve that matches the decision
Intensity is not a vanity slider. It changes how hard agents push before they stop—which directly maps to tokens, wall-clock time, and how aggressively unknowns are surfaced in your schema.
Orientation, triage, or first-pass scans
Lower cost profile for questions where you mainly need structure and pointers—not exhaustive debate across every edge case. Ideal for recurring watchlist checks that should stay economical at volume.
Everyday investigations your team runs weekly
Balanced depth for competitive snapshots, account prep, and product research where multiple sources should agree before you present externally. The workhorse setting for most campaigns.
Board memos, diligence chapters, and high-stakes decisions
Pushes agents to stress-test assumptions, chase contradictions, and fill schema sections that are usually marked unknown. Expect more tokens and longer wall-clock time—and budget accordingly.
Capabilities
Everything you need to run research like infrastructure
Deep Research is the runner; the workspace is the control plane—together they cover investigation, monitoring, and proof. Below is the surface area most teams enable within their first month.
Research campaigns
Schema, context, instructions, intensity, webhooks, and approvals—every run inherits the same quality bar.
How it shows up in-app
Campaign defaults
Watchlists & alerts
Recurring monitors, structured AND/OR rules, Slack & webhooks, and automations when conditions fire.
Automation queue
Human-in-the-loop for sensitive actions—bulk resolve, SLA hints, and full execution context.
Agent Trust
Proposals, runs, and audits for agent behavior where mistakes are expensive.
Attribution
Tie outcomes to runs and schema fields—signal your leadership can defend.
Integrations
Run webhooks, custom headers, and API-friendly workflows alongside your existing tools.
Deliverables
What “done” looks like in Radlect
A finished run is rarely a single string. Teams mix narrative, structured fields, and machine hooks so the same research can serve humans and systems without a rewrite.
Markdown narrative
Readable long-form output with headings, lists, and citations where the pipeline supplies them. Optimized for humans who need to skim, annotate, and share internally.
## Executive readout
Three vendors shifted enterprise pricing…
— Citations collapsed in product UI —
Structured extraction
Schema-aligned fields and nested objects your stack can consume—CRM updates, warehouse tables, or downstream automations without an intermediate copy-paste step.
{
"expansion_score": 0.78,
"risks": ["vendor lock-in", "latency SLO"],
"last_verified": "2026-04-12"
}PDF export
One-click packaging when you need something polished for clients, auditors, or executives who still live in attachments.
Webhooks & headers
POST results to your systems with optional custom headers—wire completion events into queues, ticketing, or internal research libraries.
Payload preview
{
"runId": "run_9f3…",
"status": "READY",
"campaign": "Q3 diligence",
"extraction": { "risk_score": 0.42 }
}Next step
Open Deep Research with your account—or tour the full workspace for campaigns and watchlists.
Ready
Your next run inherits campaign defaults
Serious depth. Sensible numbers.
We price Radlect Deep Research so teams run real investigations—not demos. Credits follow the work agents actually do; campaigns and watchlists reuse context so you are not paying to reinvent the same brief every week.
Versus bespoke analyst retainers or opaque enterprise AI contracts, the quality-per-dollar story tends to show itself once volume hits production. Tiers and limits live in-product—your usage page stays the source of truth after sign-in.
- Credits scale with intensity and pipeline length—not a flat per-seat research tax.
- Reusable campaigns amortize instructions and guardrails across many runs.
- Watchlists turn repetitive monitoring into predictable spend instead of ad-hoc spikes.
What you should expect
- Transparent usage and credits aligned to run intensity
- Schemas, campaigns, and watchlists that reduce throwaway spend
- Governance included—not a surprise “enterprise module” later
- Upgrade paths when throughput and team size justify it
Usage opens after sign-in and reflects your organization's live plan and credits.
This billing period
Intensity-weighted · in-product breakdown after sign-in
Security & operations
Built for teams who cannot pretend side effects do not exist
Research is only half the story—the other half is what software does after it reads the evidence. Radlect keeps that boundary explicit.
SEND_WEBHOOK · Partner risk summary
Watchlist: Healthcare vertical · Run #4821
Alert fired
3 rules matched · severity ≥ MEDIUM
Human-in-the-loop by design
Sensitive automations can sit behind the automation queue so approvals, notes, and retries are first-class—not an afterthought in email threads.
Audit-friendly context
Execution records surface payloads and outcomes in readable layouts for approvers. Reject and approve paths support optional notes for your internal trail.
Separation of research vs. action
Research runs produce evidence and structured fields. Distinct automation steps make it obvious when software is about to take an external action—and who cleared it.
Integrations & surfaces
Radlect meets teams where they already work—chat for humans, webhooks for systems, PDFs for stakeholders who still live in email attachments.
- Slack notifications from alert rules and watchlist events
- Outbound webhooks with configurable HTTP headers
- Campaign-level defaults for run webhooks and research parameters
- Agent Trust flows for MCP-style and in-product audit requests where enabled
Support view
FAQ topics mirrored from live evaluations
SEND_WEBHOOK · Partner risk summary
Watchlist: Healthcare vertical · Run #4821
FAQ
Common questions
Still deciding? These are the topics we hear most in evaluations—answers stay high-level here; your workspace remains authoritative for limits and behavior.
How is Deep Research different from the main Research workspace?
Deep Research is the focused runner for long investigations and follow-ups. The full workspace adds campaigns, schemas, watchlists, attribution, Agent Trust, and operational views in one navigation model—most production teams use both together.
Can we control cost when questions are exploratory?
Yes. Start with light intensity, reuse campaigns so shared context is not re-tokenized every time, and prefer watchlists for recurring scans instead of one-off mega-prompts. Usage and credits are visible in-product after sign-in.
Do governance features cost extra?
Agent Trust, the automation queue, and attribution are part of the Radlect research story—not a surprise enterprise upsell layered on later. Your plan still controls overall credits and rate limits.
What do we get beyond a markdown answer?
Schema-shaped extraction, optional PDF export, webhook delivery, monitors that re-run on a cadence, and structured alert rules. The goal is an evidence package your systems and leadership can reuse—not a single chat transcript.
Can multiple teams share one campaign configuration?
Campaigns are the right place to standardize schema, tone, intensity, and webhook defaults. Individual runs and watchlists then inherit those guardrails while still allowing per-run variables where you need them.
How do watchlists relate to Deep Research?
Watchlists execute research on a schedule or trigger pattern, evaluate alert rules against new extraction, and can enqueue automations. Deep Research remains the place to open a specific investigation end-to-end when you need depth immediately.
What happens when a run fails mid-pipeline?
Status and error surfaces are designed for operators—you can retry, adjust intensity or inputs, and inspect partial extraction when the platform exposes it. Exact behavior can vary by failure class; your workspace remains the source of truth.
Is Radlect only for English-language sources?
Many teams run multilingual sources today; effectiveness still depends on the underlying models and the clarity of your schema. Campaign instructions are the right place to spell out language and jurisdiction expectations.
Resources
Go deeper after sign-in
Documentation and API references assume you have a workspace—we still want this page to show where serious answers live once you are past the landing story.
Campaign defaults
Ready when you are.
Open Deep Research for the dedicated runner, or the full workspace for campaigns, schemas, and monitors—same Radlect account.
Alert fired
3 rules matched · severity ≥ MEDIUM
Payload preview
{
"runId": "run_9f3…",
"status": "READY",
"campaign": "Q3 diligence",
"extraction": { "risk_score": 0.42 }
}