Керівництво бачить стратегічні зрушення ринку у тижні, не в кварталі.
What it does
AI agent processes competitive signals from the past week and delivers a structured report by a fixed day — for example, every Monday morning. The team gets one document instead of five open browser tabs and call recaps.
Process steps:
- Signal collection from configured sources: competitor websites, release notes, public social media posts, press releases, changelogs, internal files with strategic notes.
- Relevance filtering — automation removes the noise: general marketing posts, personnel changes with no market impact.
- Categorization by area: product, pricing, positioning, hiring, partnerships, public statements from executives.
- Comparison with the previous period — which signals are new compared to the last digest.
- Linking to internal context via RAG — the AI agent pulls in strategic documents, OKR, and past decisions from file storage.
- Digest generation using a fixed template: executive summary at the top, details by category, a 'what to do with this' section at the end.
- Delivery in a format convenient for executives — PDF, a Notion page, email, or a message in a private Slack channel.
What automation does not do
- Does not replace strategic thinking — provides material for discussion, not ready-made decisions.
- Does not see beyond the closed perimeter — competitors' internal metrics, non-public plans, and data under NDA remain invisible.
- Does not make market forecasts — records shifts that have occurred, but does not predict their consequences.
How it works
The architecture is a three-layer pipeline: signal collection, processing, digest generation. The code is custom because weekly synthesis requires stable categorization logic tailored to the company's specific market context — generic CI platforms do not know the product specifics or current strategic priorities.
Implementation steps
- Defining the perimeter — a list of competitors and key sources for each: website, blog, changelog, public channels. The perimeter is fixed and reviewed once per quarter.
- Setting up collectors — custom parsers tailored to the specifics of each source. Generic scrapers do not work because competitor website structures vary significantly.
- Storing raw signals in file storage with versioning. Three months later, you can go back and check which signals mattered at the time and how the situation developed.
- Processing pipeline: relevance filter → categorization → diff with the previous period → linking to internal context via RAG.
- Prompt engineering for the synthesis stage. The digest template is fixed (executive summary, sections by category, actions section), while the content changes from week to week.
- Configuring delivery — email, a message in a Slack channel for leadership, or publishing a page in Notion/Google Docs.
- Run schedule — Sunday evening or Monday early morning, so the digest is waiting for the team at the start of the work week.
System components
Component | Purpose |
|---|---|
Collector | Collects signals from public sources on a schedule |
Storage layer | Stores raw signals and previous digests in file storage |
RAG module | Links signals to internal strategic documents |
Synthesis engine | Categorization, period comparison, narrative generation |
Delivery | Delivers to email, Slack, or Notion on a schedule |
Why custom code
Standard competitive intelligence platforms provide a general market overview but do not know the context of a specific company. Custom automation accounts for product specifics, positioning, and current strategic priorities. Categorization logic is configured for the business — a 'price change from competitor X' is critical for one company and noise for another.
RAG on top of internal files is an important part of the solution. The digest relies on the company's ground truth: the AI agent does not invent strategic priorities but links new signals to already documented decisions. This also addresses the second pain point — knowledge stops living only in people's heads and surfaces in structured weekly reports.
Prerequisites
Before launch, automation requires a defined competitive perimeter, internal strategic documents, and team readiness to maintain the process after launch.
Data and access
- A defined list of competitors and key sources for each — websites, blogs, LinkedIn company pages, public channels.
- File storage with internal strategic documents: current strategy, recent OKR, past analytical reports.
- API keys for LLM (AI model or equivalent with long context support).
- Access to the delivery system: work email, Slack channel for leadership, or Notion workspace.
Team readiness
- A person with strategic context — COO, Head of Product, or Head of Strategy. They define signal categories and validate the first digests.
- Leadership readiness to read the digest weekly and provide feedback in the first weeks after launch.
- A decision on who owns the process after launch — sources change, categories evolve, automation needs an owner.
Implementation timeline
Full launch — 6-10 weeks:
- Weeks 1-3: requirements gathering, defining the competitive perimeter and categories.
- Weeks 4-6: development of collectors and processing pipeline, integration with file storage.
- Weeks 7-8: calibration on live data, tuning synthesis prompts.
- Weeks 9-10: final delivery, schedule automation, documentation for the team.
Pain points
- Ongoing Executive Updates
- Knowledge in heads, not in documents
FAQ
How long does implementation take?
Full launch takes 6–10 weeks. The first three weeks go toward defining the perimeter: competitor list, sources for each, signal categories. The next three weeks cover building the collectors and the processing pipeline. The final two to four weeks are spent calibrating on live data and tuning synthesis prompts. After launch, leadership provides feedback for final adjustment of categories to the business context.
What if we don't have an organized file storage with strategic documents?
You can start with a minimum — a small folder with key documents: current strategy, a recent OKR, positioning, past analytical reports. The AI agent will work with limited context, but the digests will still deliver value. In parallel, the team accumulates and structures documents — working with automation itself becomes an incentive to bring order to knowledge.
What can break after launch?
Three typical risks. The first — changes to competitor site structures break custom collectors, which requires monitoring and updates. The second — public sources provide an incomplete picture: internal metrics, non-public plans, and data under NDA are not visible. The third — calibrating categories to the business context requires several iterations, so the first digests produce more noise than signal and stabilize as feedback accumulates.
Does automation work in our industry?
Automation is designed for SaaS/Tech and horizontal B2B segments, where competitors publish release notes, changelogs, and are active in public channels. In industries with less public transparency (enterprise software, B2G, regulated markets) there are fewer signals, but the solution structure is applicable — sources are replaced with industry-specific ones, categories are adapted, the synthesis logic and connection to internal context remain.
Can the digest run less or more frequently than once a week?
The standard is weekly: a balance between relevance and signal volume. A bi-weekly cadence works for slowly changing markets where there are few new signals in seven days. A daily cadence suits highly competitive segments, but costs more to process and produces more noise — not every day brings a strategically significant event.
Do internal documents need to be passed to an LLM?
Internal documents are used as context for RAG — the AI agent connects competitor signals to the company's current strategy. Sensitive data (detailed financials, full per-person OKRs) is filtered at the file storage level — the agent sees only what is accessible. For critical scenarios, LLMs with on-premise or private cloud deployment are suitable.
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