How to Use AI for Market Research and Competitor Analysis: A Complete Method

AI can search hundreds of pages, classify customer reviews, and build a competitor matrix in minutes. Fast retrieval, however, is not the same as reliable research. This guide provides a repeatable workflow from decision framing and market sizing to customer evidence, competitor scoring, source validation, and final recommendations, supported by a real AI-meeting-assistant case study.

Traditional market research often requires:

- Searching industry reports;

- Reading company websites and filings;

- Collecting competitor pricing;

- Organizing customer reviews;

- Designing interviews;

- Cleaning survey data;

- Comparing product capabilities;

- Writing a recommendation.

AI dramatically reduces the mechanical workload, but it also creates new failure modes:

- Plausible citations that do not exist;

- Data from different years combined without warning;

- Google Trends indices treated as absolute search volume;

- Vendor marketing repeated as independent fact;

- A few online comments generalized to an entire market;

- A conclusion written first and evidence selected afterward;

- A polished report that cannot support a real decision.

Effective AI-assisted market research is not about asking a model to “give the answer.” It is about using AI to build an auditable chain of evidence.

The governing principle is:

Define the decision before designing the research. Record evidence before writing conclusions. AI can accelerate research, but it cannot replace source verification or commercial judgment.

---

1. Why methodology matters more than one perfect prompt

A field experiment conducted with Harvard Business School and Boston Consulting Group involved 758 knowledge workers.

Across 18 consulting tasks inside the demonstrated capability frontier of AI, participants using AI:

- Completed 12.2% more tasks;

- Finished an average of 25.1% faster;

- Produced significantly higher-quality output.

On a complex task outside that frontier, however, AI users were 19 percentage points less likely to reach the correct solution than participants without AI.[1]

This result maps directly onto market research.

AI is strong at:

- Expanding keywords;

- Rapid discovery;

- Summarization;

- Classification;

- Building comparison tables;

- Finding early patterns;

- Drafting reports.

AI is less dependable at:

- Determining whether data definitions are comparable;

- Detecting intentionally vague vendor wording;

- Inferring undisclosed revenue and market share;

- Separating correlation from causation;

- Resolving conflicting sources;

- Determining whether a small review sample is representative;

- Accepting accountability for a business decision.

A complete method must therefore specify:

1. Which work can be delegated to AI;

2. Which claims require original-source verification;

3. Which findings remain hypotheses;

4. Which numbers require independent corroboration.

---

2. The complete workflow

Decision-grade market research can be divided into nine stages:

These stages should not be collapsed into “produce a complete market-research report.”

---

3. Step one: define the decision

A weak project often begins with:

Analyze the AI productivity market.

That question does not specify:

- Geography;

- Customer type;

- Product category;

- Price range;

- Decision horizon;

- Intended action.

A stronger question is:

We are considering an AI meeting assistant for U.S. professional-service companies with 10–100 employees. Determine whether the market is attractive over the next 12 months, focusing on customer demand, willingness to pay, major competitors, differentiation opportunities, and acquisition risk.

A useful research brief

Reusable prompt

```text

You are a market-research project manager.

Convert the business idea below into a research brief. Do not begin external research yet.

Output:

1. The final decision this project must support

2. Target market and customer scope

3. Research time horizon

4. Ten questions that must be answered

5. Known assumptions

6. Critical unknowns

7. Explicit exclusions

8. Recommended source categories

9. Final deliverable structure

Business idea:

[Paste idea]

```

If the AI cannot identify a concrete decision, the project is not yet properly framed.

---

4. Step two: build a hypothesis tree

Market research is not a competition to collect the most material. It is a process for testing a limited set of assumptions.

For an AI meeting assistant:

```text

Is the market attractive?

├── Is the need real?

│ ├── Are meetings frequent enough?

│ ├── Does manual note-taking create measurable cost?

│ └── Are missed decisions a serious problem?

├── Will users pay?

│ ├── What do current alternatives cost?

│ ├── Does the individual or company pay?

│ └── What price range is acceptable?

├── Can the market be reached?

│ ├── Where do users discover tools?

│ ├── Which meeting platforms control distribution?

│ └── Is enterprise selling required?

├── Is there room to differentiate?

│ ├── Are language needs underserved?

│ ├── Are vertical templates weak?

│ └── Is privacy a meaningful barrier?

└── Can unit economics work?

├── What do transcription and model inference cost?

├── Is acquisition affordable?

└── Can free users convert?

```

Select high-priority hypotheses

Prioritize assumptions that:

- Would change the final decision if wrong;

- Have high current uncertainty;

- Can be tested through evidence;

- Are affordable to validate.

AI can expand the tree. The project owner must decide what matters.

---

5. Step three: create an evidence hierarchy

Sources do not deserve equal weight.

Tier A: primary authoritative evidence

- Government statistics;

- Regulatory filings;

- Company financial statements;

- Official pricing;

- Official product documentation;

- Patents and court records;

- Formal app-store listings;

- Public job postings.

Use for:

- Pricing;

- Features;

- Revenue;

- Headcount;

- Regulation;

- Release dates;

- Official positioning.

Tier B: independent professional evidence

- Peer-reviewed research;

- Independent benchmarks;

- Industry associations;

- Reputable consulting research;

- Major business media;

- Transparent data providers.

Use for:

- Trends;

- Technology performance;

- Industry structure;

- User behavior;

- Relative competitive strength.

Tier C: customer and channel signals

- G2 and Capterra;

- App stores;

- Reddit;

- YouTube comments;

- Product communities;

- Support tickets;

- Sales interviews;

- Search suggestions and forums.

Use to discover:

- Repeated pain points;

- Customer language;

- Switching triggers;

- Adoption barriers;

- Unmet needs.

Do not use alone to prove:

- Market size;

- Overall satisfaction;

- Market share;

- Product accuracy.

Tier D: generated and derivative content

- AI responses without original sources;

- SEO aggregators;

- Method-free rankings;

- Undated summaries;

- Marketing blogs citing each other.

Use only as a discovery lead.

---

6. Maintain an evidence ledger

Every important conclusion should be traceable.

Example

Verification prompt

```text

Audit the evidence ledger below.

Tasks:

1. Identify claims not directly supported by their sources

2. Flag correlation presented as causation

3. Flag mixed years, geographies, or definitions

4. Classify every source as primary, independent, customer signal, or derivative

5. Assign high, medium, or low confidence

6. Do not add facts that are absent from the cited material

```

---

7. Step four: use AI for desk research

ChatGPT Deep Research

ChatGPT Deep Research can:

- Search the public web;

- Restrict research to selected websites;

- Use uploaded files;

- Access enabled connected applications;

- Propose a research plan before execution;

- Accept adjustments during the process;

- Produce a structured report with citations.[2]

Best for:

- Multi-source synthesis;

- Long reports;

- Combining filings and internal files;

- Projects that benefit from an editable research plan.

Gemini Deep Research

Gemini uses Google Search by default and can add:

- Gmail;

- Google Drive;

- Uploaded files;

- NotebookLM notebooks.[3]

Best for:

- Teams working in Google Workspace;

- Combining internal communication and external research;

- Existing NotebookLM collections.

Claude Research

Claude Research can search the web and access internal services through connectors. Anthropic states that connected services inherit the user’s existing permissions.[4]

Best for:

- Long-form synthesis;

- Internal and external evidence;

- Connected systems such as Google Workspace and Notion.

Use staged research

Do not ask:

Research the entire market and tell me the answer.

Use this sequence:

1. Define questions;

2. Identify the best source for each question;

3. Collect evidence in batches;

4. Extract structured fields only;

5. Check conflicts;

6. Synthesize last.

Source-discovery prompt

```text

Research the following market.

Scope:

- Geography:

- Customer:

- Time horizon:

- Product definition:

This round is source discovery only. Do not write the final conclusion.

Output:

1. Primary sources

2. Independent research and industry sources

3. Customer-review and community sources

4. What question each source can answer

5. Publication date

6. Likely bias

7. Questions for which no reliable source has been found

Prioritize official, regulatory, financial, academic, and independently benchmarked sources.

Do not use numbers without an original source.

```

---

8. Step five: size the market

Common market-size layers:

- TAM: The theoretical total market;

- SAM: The part the current product and geography can serve;

- SOM: The realistic share obtainable under current resources and competition.

Top-down method

```text

Global category size

× target geography share

× customer-segment share

× serviceable use-case share

= SAM

```

Advantages:

- Fast;

- Useful for initial orientation;

- Industry reports are often available.

Weaknesses:

- Highly dependent on category definitions;

- Adjacent products may be included;

- Produces a large number without operational meaning.

Bottom-up method

```text

Number of target companies

× average paid seats

× annual price

× expected adoption

= serviceable revenue

```

Illustrative assumptions only:

```text

50,000 target companies

× 8 paid seats

× $180 annually

× 20% adoption

= $144 million

```

The formula is correct, but each input is a hypothesis until validated.

Value-based method

```text

Time saved per employee

× labor cost

× share of value captured

× number of users

```

Useful for:

- Enterprise software;

- Automation;

- Labor-saving products.

The U.S. Small Business Administration recommends examining demand, market size, economic indicators, customer location, market saturation, and current prices.[5]

Useful sources include:

- Census Business Builder;

- National statistics agencies;

- SEC EDGAR;

- Industry associations;

- Company filings;

- App stores;

- Verified third-party datasets.

SEC EDGAR provides free access to more than 20 years of corporate filings. Census Business Builder provides demographic, economic, and competitive data organized by industry and geography.[6][7]

Correct use of AI in sizing

AI can:

- Build the model;

- Normalize currency and units;

- Run sensitivity tests;

- Create high, base, and low scenarios;

- Identify missing variables.

AI cannot prove that an assumption is true.

---

9. Do not misread Google Trends

Google Trends is useful for:

- Direction of interest;

- Regional differences;

- Seasonality;

- Related searches;

- Emerging terminology.

Google explains that:

- Trends uses a sample of Google searches;

- The data is normalized;

- Values are indexed from 1 to 100;

- 100 represents the relative peak for the selected time and location;

- The values are not absolute search volume;

- Low-volume terms may appear as zero.[8]

Incorrect:

Topic A is 100 and Topic B is 50, so A receives exactly twice as many searches.

Correct:

Under the same geography and date range, Topic A has higher relative search interest than Topic B. The chart does not reveal absolute query volume.

Combine Trends with:

- Estimated keyword volume;

- Website traffic;

- Registrations;

- Sales leads;

- Community discussion;

- App downloads;

- Interviews.

---

10. Step six: conduct customer research

Secondary research cannot fully explain:

- Why people buy;

- Why they quit;

- Which problem is painful enough;

- Why current tools fail;

- Who controls the budget;

- Who blocks procurement.

Interviews

Start with five to eight interviews per clearly defined segment to discover recurring patterns, not statistical prevalence.

Do not ask:

Would you buy an AI meeting assistant?

Ask:

- When was the last time something important was lost after a meeting?

- How do you record meetings today?

- How long does one summary take?

- Which tools have you tried?

- Why did you stop paying?

- Who approves recording software?

- What would you do if the current solution disappeared tomorrow?

- Which error would make you stop using the product immediately?

Review mining

Collect from:

- G2;

- Capterra;

- App Store;

- Google Play;

- Chrome Web Store;

- Reddit;

- YouTube;

- Product communities.

Use a consistent coding scheme:

Review-analysis prompt

```text

Analyze this set of real customer reviews.

Requirements:

1. Do not infer market percentages that are not in the data

2. Tag each review by use case, job, pain, buying barrier, and alternative

3. Allow multiple tags per review

4. Preserve the original review ID

5. Separate frequency from severity

6. Provide representative evidence for every pattern

7. Do not generalize a small sample to the whole market

```

Surveys

Use surveys to validate hypotheses discovered through interviews.

Measure:

- Current behavior;

- Problem frequency;

- Time or monetary loss;

- Existing alternatives;

- Payer;

- Price sensitivity;

- Procurement blockers;

- Company size and role.

Avoid relying on “Are you interested?”

---

11. Step seven: identify competitors correctly

Competitors are not limited to products with similar interfaces.

Direct competitors

Same customer, same job, similar business model.

Examples:

- Otter;

- Fireflies;

- Fathom.

Indirect competitors

Same job, different product form.

Examples:

- Notion AI Meeting Notes;

- Zoom AI Companion;

- Microsoft Teams intelligent recap.

Substitutes

What customers actually do when they do not buy a dedicated tool:

- Manual notes;

- Outsourced transcription;

- Post-meeting summaries written by staff;

- Recording without processing;

- Generic speech-to-text.

Potential entrants

Companies with distribution, data, or workflow control:

- Google Workspace;

- Microsoft 365;

- CRM platforms;

- Video-conferencing platforms.

The most dangerous competitor may not look like your product. It may already own the workflow.

---

12. What to collect about competitors

Use seven categories.

Product

- Core job;

- Target user;

- Platform;

- Languages;

- Features;

- Integrations;

- Technical limits.

Pricing

- Free allowance;

- Monthly and annual price;

- Billing unit;

- Usage caps;

- Add-on fees;

- Enterprise pricing.

Positioning

- Homepage headline;

- Primary value proposition;

- Featured customer;

- Case studies;

- Capabilities not emphasized.

Distribution

- SEO;

- App marketplaces;

- Partnerships;

- Product-led sharing;

- Freemium;

- Enterprise sales;

- Community and content.

Customer evidence

- Most common praise;

- Most common complaint;

- Cancellation triggers;

- Switching sources;

- Trust concerns.

Organization and capital

- Funding;

- Employees;

- Hiring priorities;

- Acquisitions;

- Leadership;

- Enterprise customers.

Risk

- Regulation;

- Privacy;

- Data residency;

- Platform dependency;

- Model cost;

- Accuracy;

- Channel concentration.

---

13. Step eight: build a scoring model

Competitor matrices often fail because they contain dozens of feature checkboxes but do not support a decision.

Weights must be designed for one target customer.

Example: a 20-person multilingual SaaS sales team

Scored from one to five:

This is not a global ranking. It is one model for one team.

---

14. Worked example: AI meeting assistants

The example uses current official pricing and help documentation.

Public comparison

Current official evidence:

- Otter lists 300 monthly minutes on Basic, annual Pro pricing around $8.33 per user per month, and six supported transcription languages.[9][10]

- Fireflies provides unlimited transcription and summaries on Free, with 400 minutes of team storage. It supports more than 100 languages, while multilingual mixed-language mode covers more than 60. Annual Pro pricing is $10 per seat per month, and advanced AI uses credits.[11][12][13]

- Fathom’s individual plan includes unlimited recordings and transcripts. Team pricing is $15 per user per month annually, it supports 38 languages, and it provides cross-meeting Ask Fathom and MCP access to ChatGPT and Claude.[14][15][16]

- Notion AI Meeting Notes requires Business or Enterprise for normal use. Business is publicly listed at $20 per member per month. Meeting Notes supports 16 languages and a 10-hour daily limit per user, while speaker labeling currently supports English only.[17][18]

Why Fireflies leads for this target

For a multilingual SaaS sales team, Fireflies offers:

- The broadest language coverage;

- A mixed-language mode;

- Free transcription and summaries;

- Multiple capture channels;

- Sales-oriented workflow automation.

However, advanced AI credits create an additional cost layer at scale.

Why Fathom is close

Fathom’s free individual plan is unusually strong:

- Unlimited recording;

- Unlimited transcription;

- 38 languages;

- Cross-meeting queries;

- Integration with general AI tools.

For consultants and smaller sales teams, it may be the practical winner despite ranking second in the weighted model.

Why Notion is not first

Notion’s value extends beyond transcription:

- Notes flow directly into projects and knowledge bases;

- Meeting evidence connects with pages, databases, and other applications;

- The same workspace can produce reports and tasks.

If the organization is already standardized on Notion, raising the “knowledge use” weight would significantly improve Notion’s ranking.

This demonstrates:

The result is determined by the weights, and the weights are determined by the decision.

Why Otter may still win

Otter has fewer languages, but provides:

- Mature meeting search;

- AI Chat;

- Vocabulary and speaker features;

- Google Drive connectivity;

- Sales, recruiting, and media agents.

For an English-first organization that values a mature meeting workflow, multilingual coverage should receive less weight and Otter’s score would rise.

---

15. Prevent scoring manipulation

Set weights before viewing outcomes

Adjusting weights after seeing the winner turns the model into a justification tool.

Define score thresholds

Example for multilingual support:

Do not reward undisclosed information

If a vendor does not publish:

- Accuracy;

- Security audit;

- Usage limits;

- Data residency;

mark it as unknown. Do not assume the absence of a limitation.

Run sensitivity analysis

Increase and decrease major weights by 20%.

If a minor adjustment moves the winner to fourth place, the conclusion is unstable and should be expressed as scenario-specific recommendations rather than one champion.

---

16. Convert data into insight

Weak analysis repeats facts:

Fireflies supports more than 100 languages; Fathom supports 38.

Useful insight answers what the facts mean:

For multilingual sales teams, language coverage determines whether one workflow can scale across countries. However, Fireflies charges credits for advanced AI actions, so total cost at scale must be modeled separately.

Use four levels:

1. Fact: What happened?

2. Pattern: What connects several facts?

3. Explanation: Why is the pattern appearing?

4. Decision implication: What should the company do?

Example:

Facts:

- Most products have a free tier;

- Advanced AI and organization-wide search are paid;

- Meeting features are appearing in platforms such as Notion.

Pattern:

- Basic transcription is becoming commoditized;

- Competition is shifting from “Can it transcribe?” to “What happens after the meeting?”

Explanation:

- Speech-recognition costs have fallen;

- Platform vendors possess distribution;

- Independent tools need CRM, vertical workflows, or automation.

Implication:

- A new product should not lead with transcription alone;

- It should focus on vertical use cases, workflow closure, or compliance;

- Pricing should connect to business outcomes rather than minutes.

---

17. Require a counterargument pass

Do not publish the first synthesis.

Use a separate model or conversation as an adversarial reviewer:

```text

You are an investment committee member responsible for rejecting this project.

Review the market research and identify:

1. Fragile assumptions

2. Ignored substitutes

3. Possible double counting in market size

4. Vendor claims treated as fact

5. Stale data

6. Sample bias

7. Statements unsupported by sources

8. The three most likely reasons the market entry would fail

Do not rewrite the report. Output only counterarguments and required evidence.

```

Minimum evidence by claim type

---

18. Recommended research-report structure

Executive summary

- Recommendation;

- Three core pieces of evidence;

- Three major risks;

- Next action.

Scope and question

- Geography;

- Customer;

- Definition;

- Time horizon;

- Exclusions.

Customer demand

- Jobs;

- Trigger events;

- Pain severity;

- Alternatives;

- Budget owner.

Market size

- TAM;

- SAM;

- SOM;

- Scenario range;

- Assumptions.

Trends and drivers

- Technology;

- Regulation;

- User behavior;

- Distribution;

- Cost.

Competitive landscape

- Direct;

- Indirect;

- Substitutes;

- Potential platform entrants.

Competitor matrix

- Product;

- Price;

- Customer;

- Positioning;

- Distribution;

- Strengths;

- Weaknesses;

- Evidence.

Customer research

- Sample;

- Method;

- Patterns;

- Counterexamples;

- Representative quotes.

Opportunity and risk

- Unmet needs;

- Defensibility;

- Channel risk;

- Cost risk;

- Compliance risk.

Recommendation and validation

- Enter, adapt, or stop;

- Target customer;

- MVP;

- Pricing test;

- 30/60/90-day plan;

- Stop conditions.

---

19. Reusable full-research prompt

```text

You are a rigorous market-research analyst.

Research topic:

[Market or product]

Decision:

[Business decision to support]

Scope:

- Geography:

- Customer:

- Time horizon:

- Product definition:

- Exclusions:

Requirements:

1. Produce a research plan and hypothesis tree first

2. Separate primary, independent, customer-signal, and derivative sources

3. Use official sources for pricing, features, dates, and company facts

4. Size the market using both top-down and bottom-up methods

5. Treat Google Trends as a relative index only

6. List conflicting evidence separately

7. Do not invent numbers, citations, or companies

8. Write “not disclosed” or “cannot verify” when appropriate

9. Separate facts, inferences, and recommendations

10. Deliver an evidence ledger

Final output:

- Executive summary

- Market definition

- Demand analysis

- TAM/SAM/SOM

- Trends

- Customer research

- Competitor matrix

- Scoring model

- Opportunities and risks

- Recommendation

- 30/60/90-day validation plan

- Sources and evidence ledger

```

---

20. Recommended tool stack

No single AI completes the whole process well.

Do not ask only:

Which AI is strongest?

Ask:

Which tool is best for this research stage and allows the evidence to be verified?

---

21. Common failure modes

Writing the report before collecting evidence

Correct order:

```text

Question → hypothesis → evidence → analysis → conclusion

```

Stopping after buying one market report

Industry reports may:

- Define the market too broadly;

- Hide methodology;

- Extrapolate historical trends;

- Combine adjacent categories.

Validate through customer counts and pricing.

Counting features instead of customer outcomes

Twenty checkboxes do not necessarily beat one reliable workflow.

Researching only company websites

Also inspect:

- Help documentation;

- Detailed pricing;

- Reviews;

- Status pages;

- Privacy policies;

- Hiring;

- Release notes;

- Cancellation and refund flow.

Treating review count as market share

Review volume depends on:

- Product age;

- Solicitation strategy;

- Platform;

- Customer type;

- Geography.

Ignoring dates

Record:

- Publication date;

- Access date;

- Product version;

- Geography.

Having no stopping rule

Possible rules:

- 80% of critical questions have medium- or high-confidence evidence;

- Every core number is cross-checked;

- New sources no longer change the main conclusion;

- Remaining unknowns can be tested through an MVP.

---

22. Seven-day execution plan

Day 1: frame the decision

- Research brief;

- Hypothesis tree;

- Customer and geographic scope;

- Evidence ledger.

Day 2: desk research

- Government, filing, and official product evidence;

- Market timeline;

- Source logging.

Day 3: competitor analysis

- Direct, indirect, substitute competitors;

- Pricing, features, positioning, distribution;

- Initial scoring model.

Day 4: customer evidence

- Reviews;

- Pain and switching codes;

- Interview recruitment;

- Interview guide.

Day 5: market sizing

- TAM, SAM, SOM;

- High, base, and low cases;

- Sensitivity variables.

Day 6: synthesis and challenge

- Facts to insights;

- Adversarial AI review;

- Missing evidence;

- Confidence adjustment.

Day 7: delivery

- Executive summary;

- Competitor matrix;

- Entry recommendation;

- 30/60/90-day validation plan;

- Evidence ledger.

---

23. Final quality checklist

Decision

- [ ] Supports one concrete decision

- [ ] Is not just an industry overview

- [ ] Includes an actionable next step

Evidence

- [ ] Every core number has an original source

- [ ] Dynamic facts include dates

- [ ] Vendor claims are labeled

- [ ] Major conclusions are cross-checked

- [ ] Unknown information is not presented as fact

Method

- [ ] Market definition is clear

- [ ] TAM, SAM, and SOM are not mixed

- [ ] Interview sample and limitations are disclosed

- [ ] Competitor weights were set before scoring

- [ ] Sensitivity analysis was performed

AI use

- [ ] Original sources were opened and checked

- [ ] No invented citations

- [ ] Facts, inferences, and recommendations are separated

- [ ] A counterargument review was completed

- [ ] Sensitive information was not uploaded to unapproved tools

---

24. Final assessment

AI has radically accelerated market research.

It can compress days of mechanical work involving:

- Keyword expansion;

- Source discovery;

- Document summarization;

- Review classification;

- Competitor matrices;

- Market models;

- Report drafting.

Speed, however, is not the final purpose of research.

The value depends on:

- Whether the question is correct;

- Whether data definitions are compatible;

- Whether sources are trustworthy;

- Whether assumptions are visible;

- Whether conclusions can be challenged;

- Whether recommendations support action.

Modern Deep Research systems can perform multi-step searches and produce citation-rich reports. DeepResearch Bench uses 100 PhD-level tasks across 22 fields and explicitly evaluates report quality, effective citation count, and citation accuracy.[19]

The existence of that benchmark reinforces the central issue:

- Was the search complete?

- Are citations accurate?

- Does the evidence support the claim?

- Did the reasoning go beyond the evidence?

The complete method can be summarized in one sentence:

Let AI expand the search space and reduce organization cost. Let humans narrow the decision, validate the evidence, and own the conclusion.

High-quality AI market research is not the fastest possible report. It is the fastest route to an evidence system that can be inspected, challenged, updated, and used for action.

Information was updated on June 24, 2026. AI products, pricing, features, and limits change frequently. Recheck official sources whenever current product facts affect the decision.

---

Sources

1. [Harvard Business School: Navigating the Jagged Technological Frontier](https://www.hbs.edu/faculty/Pages/item.aspx?num=64700)

2. [OpenAI: Deep Research in ChatGPT](https://help.openai.com/en/articles/10500283-deep-research-in-chatgpt)

3. [Google: Use Deep Research in Gemini Apps](https://support.google.com/gemini/answer/15719111)

4. [Anthropic: Claude Research and Integrations](https://www.anthropic.com/news/integrations)

5. [U.S. Small Business Administration: Market Research and Competitive Analysis](https://www.sba.gov/business-guide/plan-your-business/market-research-competitive-analysis)

6. [SEC EDGAR Search](https://www.sec.gov/search-filings)

7. [U.S. Census Bureau: Census Business Builder](https://www.census.gov/data/data-tools/cbb.html)

8. [Google News Initiative: Basics of Google Trends](https://newsinitiative.withgoogle.com/resources/trainings/basics-of-google-trends/)

9. [Otter Pricing](https://otter.ai/pricing)

10. [Otter Supported Languages](https://help.otter.ai/hc/en-us/articles/360047247414-Supported-languages)

11. [Fireflies Pricing](https://fireflies.ai/pricing)

12. [Fireflies Supported Languages](https://guide.fireflies.ai/articles/2973706448-learn-about-fireflies-supported-languages)

13. [Fireflies AI Credits](https://guide.fireflies.ai/articles/2114151875-learn-about-ai-credits)

14. [Fathom Pricing](https://fathom.video/pricing)

15. [Fathom Supported Languages](https://help.fathom.video/en/articles/296192)

16. [Fathom MCP Integration](https://help.fathom.video/en/articles/11497793)

17. [Notion AI Meeting Notes](https://www.notion.com/help/ai-meeting-notes)

18. [Notion Pricing](https://www.notion.com/pricing)

19. [DeepResearch Bench](https://deepresearch-bench.github.io/)