Industry Research and Strategy AI Agents

Put AI agents to work on research your team never has time to finish.

Stravida designs managed AI agents for market research, competitor monitoring, target account research, opportunity scoring, synthesis, and executive briefs, with human oversight and performance tied to verified research output.

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Managed AI workforceAnalyze, synthesize, score, compare, and brief

500K+Labor hours automated

5.7xAverage ROI

94%Client expansion rate

<30 daysTypical deployment path

Why strategy teams need leverage

01 Healthcare leaders need decision-grade research without building a research department.

Growth teams need better market, competitor, account, and service-line intelligence, but the work is too time-consuming to run manually every week.

Problems we look for first

Competitor moves, market changes, and account signals are reviewed inconsistently or too late.
Research lives across websites, PDFs, directories, news, job posts, social channels, and internal notes.
Leadership needs concise synthesis, not raw links or long reports that no one uses.
Business development and growth teams lack a repeatable scoring model for opportunities and target accounts.
Strategic research is important, but urgent execution work keeps pushing it aside.

Healthcare strategy team reviewing market research and opportunity signals

Research workflow overview

See which decision needs better intelligence.

The first step is defining the leadership decision, then building the research loop around that decision instead of collecting everything.

Before another automation pilot

Find the research workflow where AI can turn scattered signals into decision-ready intelligence.

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How it works

02 Start with one research workflow that produces a recurring leadership decision.

The first build focuses on a high-value research loop with clear sources, scoring rules, synthesis standards, and a defined audience for the output.

Define the research decision

We identify the decision the research must support, such as market selection, competitor response, target account priority, or service-line opportunity.

Map sources and scoring

We document approved sources, freshness rules, scoring criteria, exclusions, and evidence standards for the research workflow.

Configure the agent workflow

Agents are designed around exact actions such as collecting signals, comparing sources, scoring opportunities, and preparing executive summaries.

Add human review

Strategic judgments, recommendations, and high-stakes conclusions route to a human reviewer before leadership relies on them.

Measure research output

Performance is tied to briefs produced, sources reviewed, opportunities scored, time saved, and decision usefulness.

Expand the research engine

Once the first loop proves value, the operating model can expand across markets, service lines, competitors, accounts, or acquisition targets.

Managed AI workforce

A governed research workflow that turns scattered signals into useful briefs.

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What we evaluate

03 Research AI works when source rules, scoring logic, and review standards are explicit.

The agent workflow has to produce evidence-rich output that leaders can use. Raw scraping is not enough. The value is synthesis, prioritization, and decision support.

Evaluation areas

Market, competitor, job post, directory, social, website, document, and news source maps
Scoring logic for service lines, accounts, competitors, locations, and opportunity signals
Evidence standards, freshness requirements, exclusions, and source quality rules
Human review points for strategic recommendations, claims, acquisition logic, and sensitive conclusions
Current research time, output cadence, decision use, and repeated manual collection work
Outcome measures tied to briefs produced, opportunities scored, time saved, and leadership adoption

What you get

A research workflow map showing which intelligence loop is ready for AI support
The exact tasks agents can collect, compare, score, synthesize, brief, or escalate
A governance plan for source quality, evidence standards, review, and strategic judgment
A measurement plan tied to briefs produced, opportunities scored, time saved, and decision usefulness
A rollout sequence for the first research workflow and the next expansion path
A plain-English operating model leadership can review before implementation begins

Outcome-backed implementation

Build AI around decision support, not around raw research volume.

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What gets built

04 A governed research workflow that turns scattered signals into useful briefs.

The workflow is built around practical strategy jobs: analyze, synthesize, score, compare, brief, and route high-stakes conclusions for human review.

Healthcare market research dashboards with AI synthesis overlays

Market and competitor signals

Collect the signals that matter.

Agents can monitor approved sources, compare updates, and prepare evidence for market or competitor decisions.

Opportunity scoring

Rank accounts and markets with a repeatable model.

The workflow can score target accounts, service-line opportunities, or local market signals using agreed criteria.

Healthcare executives reviewing AI-assisted market research brief

Executive briefs

Give leaders synthesis, not raw links.

Agents can prepare concise briefs with sources, confidence levels, tradeoffs, and the next recommended review point.

Healthcare strategy command center reviewing research workflow outputs

Strategic review

Keep judgment with the operator.

The system prepares research output and evidence, while high-stakes strategic recommendations stay under human review.

Ready when the workflow is ready

Find the research workflow where AI can turn scattered signals into decision-ready intelligence.

Book Your Strategy Call

Healthcare operating experience

05 Build AI around decision support, not around raw research volume.

Stravida brings healthcare growth and operating discipline to research workflow design. The goal is useful intelligence your team can trust, review, and act on.

Dave NelsonChief Development Officer, Advanced UrologyLinkedIn profile

★★★★★

George is an experienced marketing professional who can uniquely blend broad medical practice marketing initiatives smoothly with operations and sales in a high growth environment. His experience with developing high tech call centers generated significant new patient volume and retention.

Hunter MeffordCo-Chief Operating Officer, Advanced Recovery SystemsLinkedIn profile

★★★★★

Before partnering with George, our practice was stuck at around $40M in annual revenue. In just two years, he helped us scale past $120M by completely transforming our patient acquisition strategy.

Gregory PlakiasChief Marketing Officer, Arista RecoveryLinkedIn profile

★★★★★

George's expertise and dedication have made a significant impact on our ability to reach those who need addiction treatment services. His strategic approach to our digital presence was both professional and compassionate.

Build the first workflow

Find the research workflow where AI can turn scattered signals into decision-ready intelligence.

Book Your Strategy Call

FAQ

Industry research and strategy AI questions

These answers explain where Stravida looks for AI-ready research workflows, how human review works, and how performance-backed implementation is measured.

What can research and strategy AI agents do?

They can support defined tasks such as source monitoring, competitor comparison, target account research, opportunity scoring, market synthesis, and executive brief preparation.

Does this make strategy decisions automatically?

No. The workflow prepares evidence, scoring, and synthesis. Strategic decisions and recommendations remain under human review.

How do you avoid low-quality research output?

The workflow defines approved sources, freshness rules, evidence standards, exclusions, scoring logic, and human review points before agents produce recurring briefs.

Where is a good starting point?

Good starting points include competitor monitoring, target account scoring, market expansion research, service-line opportunity research, and recurring leadership briefs.

What does performance-backed mean here?

It means the workflow is measured by verified research output, such as briefs produced, sources reviewed, opportunities scored, time saved, and decision usefulness.

How fast can a first workflow go live?

A typical path can begin in under 30 days when sources, scoring rules, and review expectations are clear.