Hours of research, compressed to minutes
AI research agents for competitive analysis, market research, and account research, run at a depth and scale manual digging can't match.
An AI research agent automatically gathers, verifies, and structures information from defined sources for a specific research task - competitive analysis, account research, or market scanning - compressing hours of manual digging into minutes.
What is AI Research Agents?
We build agents that run a structured research process against your defined sources - competitor sites, public filings, news, account and prospect data - and return a formatted, cited output, instead of a person manually opening dozens of tabs and taking notes. It doesn't replace analysis or judgment; it replaces the mechanical part of research, which is finding and organising the raw material your analysis depends on.
Manual research doesn't scale past a handful of targets before it becomes the bottleneck - a team can deep-research five accounts a week by hand, but not fifty. It also fixes consistency: a researcher checks fewer sources on a Friday afternoon than a Monday morning, even without meaning to, while an agent runs the same process every time.
It's different from a scraper, which pulls raw data with no verification or structure - a research agent gathers from multiple sources, cross-checks where possible, and formats the output for direct use.
Common problems we solve
Research doesn't scale past a handful of targets
A team can deep-research five accounts a week by hand, but not fifty, so most accounts get less context than they need.
Quality drops with fatigue
A researcher checks fewer sources on a Friday afternoon than a Monday morning, even without meaning to.
Source quality varies by target
A well-documented public company is easy to research; a private company with minimal web presence is not.
Claims nobody can verify
Skipping citation tracking means nobody can trace a claim the research surfaced back to its source.
Unclear research scope
Structured output only helps if the research question was scoped clearly enough to know what "done" looks like.
Everything covered in this service
Competitive & Market Research Automation
Recurring competitor monitoring and market landscape scans, with changes flagged instead of buried in a re-run report.
Account & Prospect Research Agents
Structured account profiles and prospect research aligned to your actual qualification criteria.
Source Verification & Citation Tracking
Every claim traceable to its source, with confidence flagging for anything unverifiable.
Structured Output Formatting
Output formatted for direct use by sales or marketing, with consistent fields across every target.
Scheduled Recurring Research Runs
Research refreshed on a defined cadence instead of going stale.
Our optimization framework
Define Scope
We define exactly what the agent is researching, which sources count, and what a complete answer looks like.
Design Verification
Source priority and citation requirements get set before the agent runs, so every claim can be traced back.
Design Output
The structured output gets built around how sales or marketing will actually use it, not a generic report template.
Pilot & Spot-Check
The agent runs against real targets and its output gets spot-checked against manual research before it's trusted at scale.
Schedule & Monitor
Recurring runs get scheduled, and source quality and accuracy continue to be monitored after launch.
What actually changes
| Before | After |
|---|---|
| Five accounts researched deeply per week | Dozens of accounts researched to the same depth |
| Sourcing depends on whether the researcher kept notes | Every claim traceable to a cited source |
| Research quality varies with time of day and workload | Same process runs consistently regardless of volume |
| Competitor changes caught only during a manual re-check | Pricing and positioning changes flagged as they happen |
What we report on
Source coverage
Measured by citation completeness per research output.
Turnaround time
Tracked from research request to delivered, structured output.
Accuracy
Checked against manual spot-checks during the pilot period.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
The bottleneck in most research workflows isn't the analysis step, it's the gathering step - and gathering is the part that's actually mechanical enough to automate reliably. Teams that try to automate the judgment calls first usually get worse results than teams that just remove the tab-opening and note-taking.
How AI Research Agents connects with AI search
AI Research Agents ├── AI Sales Agents ├── AI Marketing Agents ├── Custom AI Agents └── Marketing Intelligence
AI Sales Agents
Uses account research output as context for outreach and qualification, once the research itself is already gathered.
AI Marketing Agents
Draws on the same competitive and market research to inform campaign and positioning decisions.
Custom AI Agents
Extends the same agent-building approach to a research task specific to your business, beyond the standard scope.
Marketing Intelligence
Turns the structured research output into an ongoing intelligence view rather than a one-off report.
Related AI Agents services
Agents That Actually Do the Work, Not Just Suggest It
AI marketing agents scoped to one repeatable task at a time, with a human checkpoint before anything ships.
AI Sales AgentsNever Let a Lead Wait Hours For a First Response
AI sales agents that qualify, follow up, and schedule, then hand off to a human rep at exactly the right moment.
Close the Repeat Tickets. Escalate What Needs a Human.
AI support agents that close out tickets that repeat constantly and hand off anything genuinely novel cleanly.
Custom AI AgentsGet an Agent Built Around Your Workflow, Not a Template
Custom AI agents for internal or customer-facing workflows off-the-shelf platforms can't handle.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
No, and that's not the goal. The agent handles gathering, verifying, and structuring information - the judgment calls about what the research means for strategy still need a person looking at the output.
The agent flags low-confidence or unverifiable fields rather than guessing or fabricating a plausible-sounding answer. A thin public footprint produces a thin research output, honestly labeled as such.
No - the agent is only as accurate as the sources it draws from, and public information is sometimes outdated or wrong at the source. What we build in is citation tracking and confidence flagging, so inaccuracy is traceable and visible rather than hidden.
Whatever cadence matches the use case - some competitive monitoring runs weekly, some account research only needs a refresh when a trigger event occurs. This gets scoped to your actual need rather than run on a default schedule.
Whatever your team already uses it in - a CRM field, a structured document, a spreadsheet export. We design the format around where the research actually gets consumed, not a generic report layout.
No - a scraper pulls raw data with no verification or structure. A research agent gathers from multiple sources, cross-checks where possible, and formats the output for direct use, which is a different and more involved process than extraction alone.
Manual research not keeping up with how many accounts you need to cover?
We'll scope what a research agent can reliably cover for your specific use case before recommending a build.
Ready to get started?
We usually reply within 24 hours.
AI Research Agents, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Research Scope | A defined set of sources, fields, and update cadence for the research task |
| Agent Build | A working research agent producing structured, cited output on that scope |
| Output Format | Research delivered in the format your sales or marketing team actually uses |
| Monitoring | Ongoing accuracy checks and source-quality review as the agent runs |
How this compares
| AI Research Agent | Manual Research |
|---|---|
| Scales to dozens of accounts at the same baseline depth | Depth typically drops as target count increases |
| Every claim traceable to a cited source | Sourcing depends on whether the researcher kept notes |
| Consistent process regardless of time of day or workload | Quality varies with researcher fatigue and time pressure |
An agent doesn't out-analyse a skilled researcher - it removes the ceiling on how many targets can get research at all.
What we measure this against
- Source coverage and citation completeness per research output
- Time from research request to delivered, structured output
- Accuracy rate against manual spot-checks during the pilot period
Where this applies
- A sales team wants every enterprise prospect researched to the same depth before a first call, not just the accounts a rep had time for
- A marketing team wants ongoing visibility into competitor pricing and messaging changes without a recurring manual audit
- A strategy team needs a structured market landscape scan refreshed quarterly instead of rebuilt from scratch each time