Agent · market intelligence
The market changes faster than the team can read it.
You need conclusions, not a stream of links.
The Research agent monitors competitors, prices, news, jobs, regulation and industry signals. The executive receives not a list of publications but a short conclusion: what changed, why it matters, what actions to consider. The agent also helps turn real company news into content for the website, social media and press.
Intel: conclusions, not linksNot «18 new posts about competitors», but «X dropped prices by 12%, Y launched a new line, Z lost two CTOs to LinkedIn».
Content: for site, social, mediaWith company news, history and DB at hand, the agent prepares the news agenda and content feed for all channels. Not «a freelance copywriter», but a continuous flow based on real data.
Memory: company capitalSix months in, the agent remembers «this competitor had a third relaunch this year», and at the same time: which of the company's publications worked best.
Why we built this: and how strategy fits in.
The quality of strategic decisions depends on complete and current market data. In our work with mid-market and enterprise teams, we repeatedly see decisions based on stale news. Someone still has to collect, read and turn the information into conclusions, and leaders rarely have enough time for that.
Companies usually hire an analyst, subscribe to industry newsletters or assign monitoring to marketing, but the analyst may leave with the accumulated observation history. Newsletters quickly become noise, marketing has other priorities and manual competitor reviews happen irregularly. By the time a review is ready, an important change may already have happened.
The research agent solves both: removes manual assembly from a human and accumulates memory of observations outside one analyst's head. Six months in you have not «a feed of links» but a structured market history: what changed, when, what conclusions were drawn, which played out and which didn't. That's no longer «being informed», that's a base for strategic decisions.
And why now
Market intelligence after a year: is already an asset.
Over 12 months, the agent builds a history of competitor moves, decisions that worked and signals that preceded change. This retrospective helps compare new events with the past and test conclusions. It forms gradually from the data in your specific market.
Seven effects: at the company level.
The research agent doesn't change one analyst's work. It changes how the company sees the market: how fresh, how cohesive, how historically grounded. And how quickly that picture turns into decisions.
Decisions on a fresh picture
Not «let's discuss it at the December strategy off-site», but «X happened yesterday, today we're discussing what to do». The company's reaction speed grows not from effort, but from having information at the right moment.
Competitive intel doesn't depend on one person
When an analyst leaves: understanding of «how this competitor usually moves» tends to leave with them. With the agent that memory stays with the company: accumulated observation history, competitor movement patterns, reasons behind their past decisions. It can't be reproduced in a week with a new hire.
Marketing and product work from the same map
Before the agent, marketing has its picture, product has its own, sales has theirs. Each department gathers what matters to them: and nobody has the whole picture. The research agent gives a shared source: a weekly brief everyone references.
Early signals: before it becomes a problem
The agent tracks competitor price changes, key executive moves and new regulatory requirements. Instead of hearing the news from a partner three months later, the signal appears in the next digest. It comes with a brief assessment of the possible impact on the company.
Less noise in the leader's communications
Before: 30 Telegram channels, 5 email digests, someone shares «check out this article». Now: one page once a week, with what actually matters. The leader's time returns to decisions, not reading.
Content marketing becomes a systemic process
The agent uses company news, history, databases and product materials to prepare content. It drafts material for the website, social channels and media against one plan, while the team reviews and publishes it. The content is based on real business data rather than a random external agenda.
Observation history becomes management capital
After a year you have a structured chronicle: what changed in the market, which signals preceded, which conclusions proved right and which didn't, which publications resonated best. This is the base for the next decisions: and at the same time training material for new analysts and marketers.
Five changes: for whoever watches the market.
Whoever watches competitors in your company: owner, marketer, analyst, product manager: five changes in daily work.
Mornings without crawling 30 channels
The first hour used to go into Telegram, news, competitor sites and LinkedIn. Now the analyst opens one digest with new and important signals. The same page shows what needs attention today.
Less «information burnout»
The analyst/marketer/owner stops feeling «I have to read everything not to miss anything». The agent does that. Time frees up for analysis and decisions, not reading.
Conclusions instead of links
Not «here are 20 links»: but «on this topic X, Y, Z happened; conclusion: a competitor likely preparing a launch, worth re-checking in a month». Less «read and figure out what matters», more «decide based on it».
Memory doesn't depend on your notebook
«I saw this somewhere three months ago, don't remember where»: every analyst's regular line. With the agent, memory is searchable. You can ask «when did X last change prices and by how much»: and get an answer in seconds.
Freedom to take leave without losing context
Before: leave for two weeks: miss two key events. With the agent: come back, open the period summary, see what happened, restore context in 15 minutes. No 200 unread Telegram messages.
Ten actions: intel plus content production.
So all the effects don't sound abstract: ten specific actions, split into five stages: from raw market monitoring to producing the company's content.
· Monitoring
Watches agreed sources
Competitor sites, RSS, industry news, open data, social with allowed access, regulatory publications. By schedule or by trigger.
Filters relevant items by company rules
Not «everything», but by agreed criteria: your industry, regions, competitors, product categories. Noise is filtered at the door.
· Grouping and analysis
Groups signals by topic
Not 50 links in a heap, but «competitor X: 3 events», «regulation: 2 changes», «prices in category Y: trending down». Structure instead of a feed.
Makes short «what this means» conclusions
Under each group: a short conclusion. Not a rehash of articles but interpretation: «competitor likely preparing a launch», «market reacting to regulation», «prices continue trending down».
· Delivery and memory
Prepares daily/weekly digest
The digest arrives in your chosen format: Telegram, email, dashboard or document. Delivery follows an agreed schedule, such as Monday at 9 AM. Different roles can receive tailored versions.
Alerts on critical triggers
Don't wait for the digest to learn about something important. «Competitor cut price», «mention in negative context», «new player in the niche»: comes immediately.
· Company content · the other side of intel
Forms the company's news agenda
With company news, product releases, project history, client DB and cases: the agent prepares the news agenda with priorities: what to publish this week, which storylines to develop, what content to prepare for a feature launch. Not «a quarterly Excel content plan», but a living feed.
Prepares content for the site, social and media
Draft posts for LinkedIn, Telegram, VKontakte; press releases for media; blog articles; news for the website; pitch materials for journalists. Each piece: based on real company data, for the chosen audience and channel. The marketer reviews and publishes.
· Memory and patterns
Maintains memory of observations
All gathered signals: about the market and about the company's own content effectiveness: stay in a structured base. You can search back: «when did X last change prices», «which of our LinkedIn posts got the most views and why».
Accumulates «typical moves» for each player
Six months in, the agent knows patterns: «this competitor likes to launch at quarter-start», «that company usually replies to our promos in 2 weeks», «our audience prefers case studies to product announcements». No longer data: understanding of the market and your audience.
What we plug in: and what you get
The Research agent closes two jobs at once: market intel and content production. Left: where it gets signals, right: what it turns them into.
Intel + content
Monitors the market 24/7 and produces content in parallel: posts, digests, analytics
- Monitors topical sources 24/7
- Filters noise, keeps only what's relevant
- Prepares daily and weekly digests
- Produces content: posts, articles, newsletters
- Escalates signals and risks to the board
You don't hire an analyst and a content manager separately. One agent holds both functions: sees what's happening on the market and turns it into content for you.
Where we plug in: six points of the intel stack.
The research agent works only with sources you have legal access to. You pick the specific set of sources and channels: we help understand what's technically reasonable to plug in first and what's better to defer.
Sources
Where the agent gets signals. Only allowed and publicly available. No promises to bypass auth or scrape forbidden sources.
Observation storage
Where structured memory is stored: so you can search back and see dynamics.
Delivery channels
Where the leader and team read results. Different formats and channels for different roles.
Automation
Schedule of pickups and triggers for critical events.
AI stack
For classification, summarization, deduplication, conclusions. Models chosen for task and jurisdiction.
Quality control
To avoid «hallucinations» and noise: confidence thresholds, review-step, feedback loop.
For the first contour, 1–2 monitoring directions and a limited source list are needed. Expansion: at the implementation stage, once the first contour proves value.
Three levels: not three pricing tiers.
A junior analyst costs a company 100–150K RUB per month with taxes, or more than 1.5M RUB per year. The research agent costs 300K RUB for the first contour, 600K RUB for implementation and 40K RUB per month for maintenance. It also it doesn't carry the observation memory away when it «leaves».
Minimum working contour
The first contour covers one or two monitoring areas and a limited source list. We configure relevance rules, exclusions, the digest format and minimal topic memory. We test the setup on real editions.
The goal of the first contour is simple: in 2–3 weeks you understand whether the agent turns noise into conclusions. If not, we'll say so.
Full intelligence for the whole company
- ▪Extended source list (dozens)
- ▪Recurring cron jobs and triggers
- ▪Categorization and prioritization of signals
- ▪Cumulative observation memory and search over it
- ▪Alerts on critical triggers
- ▪Reports for different roles: leader / marketing / product
- ▪Integration with internal systems
Closed sources and a private contour
If monitoring involves closed sources, internal data, or the agent must work with internal intel across departments: that's enterprise. Requires private deployment, local model, strict history storage and audit logs.
Footnote · maintenance
From 40K RUB/month
Sources change structure, platforms restrict access, new competitors and topics appear. Maintenance covers: parser and relevance-rule adjustments, adding new sources, report-format adaptations, conclusion quality monitoring, local model within the limit.
When the research agent isn't your fit.
If you don't have a process for acting on conclusions: define one first. The research agent prepares a digest, but it creates value only when someone reviews it regularly and acts on it. Before starting, assign an owner, a review cadence and the kinds of decisions it should support.
If your market is a niche of 5 companies you already know inside out: better to spend on in-person meetings with those 5 than on an agent. The research agent shines on volume: dozens of sources, hundreds of signals, a real filtering task.
If you expect «bypassing auth» or scraping closed data: this isn't our format. We work only with legal access: public sites, allowed APIs, paid subscriptions, open data. No promises of «pulling from a competitor's private dashboard».
If you expect an «AI analyst that makes strategy itself»: strategic decisions will still remain with your team. The agent prepares a current picture and surfaces signals. You decide how to use the information.
If any of the above describes you, mention it on the first call. We'll propose a different configuration or honestly point you to a different tool.
Training after launch
Your team should be confident with the new tool
Analysts learn to set source criteria, verify findings and separate useful market signals from noise.
See training optionsDescribe your market: we'll respond with an analysis within 2 hours
We reply during business hours. On the first review we'll say where an agent can pay off, which metric proves it, and where budget is better not spent.