Agent · market intelligence
The research agent — this is intelligence plus content production,
not a news Telegram channel.
The research agent works in two directions. Inward: monitors competitors, news, prices, regulation — and gives the leader «what changed and why it matters» conclusions to make decisions on. Outward: with access to company news, history, DB and product materials, the agent forms the news agenda, prepares a content feed for the website, social and media. Now that content marketing is part of competitiveness, this is critical.
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.
Any strategy is, at its core, a series of decisions on a market picture. A good strategy = fresh, full picture. A bad one = outdated, fragmented. We have worked with mid-market and enterprise for years and see the same plot over and over: decisions get made on stale news. Not because the owner is lazy — because there's physically no fresh picture. Someone has to assemble it, someone needs time to read, someone has to draw conclusions.
Familiar solutions — hire an analyst, subscribe to industry mailings, assign marketing — work poorly. An analyst quits and takes all the memory of observations with them. Mailings become noise. Marketing is busy with something else. And «ask the guys to look at competitors» is at most once a quarter, and by then the competitor has already done what they wanted.
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 picture: how competitors moved, what worked and didn't, which signals preceded changes. You can't buy this — those who start later won't have this retrospective. You will. This is the «company's accumulated individuality» we mention across all agents: the earlier you start, the stronger the advantage. Latecomers get the same tool, but not the accumulated history of their 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
A competitor cut price in one region. A key exec left a neighboring company. Regulation changed. Before — you'd hear about it 3 months later from partners. Now — next morning in the digest, with a ready conclusion «what this means for us».
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
Content is now part of company competitiveness. Before: the news agenda is collected by a copywriter ad-hoc, social posts get written «when the marketer has time». Now: the agent has access to company news, history, DB, product materials — and prepares a continuous content flow for the website, social and media. Not «a freelance copywriter», but a permanent production line based on real business data.
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
Before, the first hour: Telegram, news, competitor sites, LinkedIn. Now — open the agent's digest. What's new, what matters, what to react to today. All on one page.
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
In your chosen format: Telegram, email, dashboard, document. At the right time — e.g. Monday 9 AM. With different versions for different roles if needed.
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 pilot, 1–2 monitoring directions and a limited source list are needed. Expansion — at the implementation stage, once the pilot proves value.
Three levels — not three pricing tiers.
Easy to compare. A junior analyst on payroll costs the company 100–150K/month with taxes. Per year — 1.5M+. Research agent: pilot 300K in 2–3 weeks, implementation 600K, maintenance 40K/month. And it doesn't carry the observation memory away when it «leaves».
Minimum working contour
1–2 monitoring directions. A limited source list. Relevance and exclusion rules. Digest format for your team. Minimal topic memory. Tested on real publications.
Pilot goal — 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 — the agent is pointless. The research agent gives you a digest. If it sits in a drawer with nobody reacting — there's no point collecting it. First answer: who reviews the conclusions, when, and which decisions follow.
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» — this isn't the research agent. Strategic decisions stay with your team. The agent prepares a fresh picture and surfaces signals. What to do with that picture — you decide.
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.
We don't just deploy — we teach people to use AI properly.
In 2–3 years AI tools will be the standard in engineering and business teams. Whoever starts learning now will gain a competitive advantage that latecomers won't have. That's why we have a separate direction: training client teams, so that AI tools and agents don't become «a toy for 3 months», but turn into part of the working process.
Corporate programs for adopting AI in development
For company engineering teams that want to switch to the AI-native approach. A 2–4 week program: review of the team's stack, selecting AI tools for the tasks, training on using them, ramp-up to regular production use. Not a «theoretical course», but a real shift in how the team works.
CTO, tech leads, developers · teams 5–50+
Client team onboarding for working with AI agents
Once we've deployed Sales, Personal, Support, RAG or another agent — we train your team to work with it well. How to adjust scenarios, write prompts for typical tasks, handle complex cases, give the agent feedback for improvement. Without this even a well-deployed agent gets «abandoned» in 3 months.
Client operational teams · typically after agent deployment
«Where to start with AI» mentor sessions for executives
Short individual sessions with CEOs, CTOs, business owners: which AI tools actually work, where to start in your company, how to link AI to business goals, how not to burn budget on «let's try something trendy». Sessions — 90 minutes, with a concrete roadmap as the output.
CEO, CTO, owners · 90-minute sessions
Open learning materials in the Telegram channel
A free column in our Telegram channel @dxaiblog. AI tool reviews, practices from real projects, mistakes and how to avoid them, reviews of new models and platforms. Anyone can subscribe — engineer, owner, product manager. This is the public part of our education, no commitment.
Anyone exploring AI · public channel · free
Training cost is discussed individually — depends on team size, format (online/hybrid/corporate visit), program depth, and whether you need a process built from scratch or accompaniment of an existing one.
If you want to start small — subscribe to the Telegram channel @dxaiblog. All materials there are free. That's our «step zero» of training.
Describe your market — we'll respond with an analysis within 2 hours
We reply within 2 hours during business hours. In the pilot call we'll show where the agent pays off and where it shouldn't be tried.