At Rocket Tech, we are used to building complex solutions for clients, but we realized that our own internal processes were consuming too much expert capacity on tasks that could be automated. We decided to invest in ourselves and built a digital environment where AI agents take on a significant share of operational work. Below is a breakdown of how our key AI products work.
Rocket Crew: an AI developer and autonomous team member
Problem: Traditional development suffers from resource shortages and slow processes: senior developers spend up to 40% of their time on routine code review, onboarding new hires takes weeks, and backlog tasks pile up faster than the team can close them. Meanwhile, overnight outages and production bugs often go unnoticed until the start of the working day, leading to financial losses.
Solution: Rocket Crew is an autonomous AI engineer that integrates seamlessly into the codebase, task trackers and team chats. It works as a fully fledged middle developer: it requests access on its own, takes tasks from the backlog, writes production code, designs architecture and closes sprints, operating 24/7.
Use cases:
- CTOs / Tech Leads: Delegating routine feature development, refactoring and migrations, automating first-pass code review and significantly reducing the bus factor, since the AI remembers every line of the company’s code.
- Product teams / Project Managers: Instantly scaling the development team for urgent tasks without lengthy hiring, automated backlog grooming and a faster release cycle.
- QA / Support teams: Round-the-clock error monitoring, proactive alert analysis and real-time incident resolution before users notice.
Business impact:
10x faster development
Rocket Crew radically changes the speed at which value reaches the product. While the team rests, the AI engineer keeps writing code autonomously, paying down technical debt and preparing merge requests. This removes bottlenecks in production and lets you ship updates many times faster than competitors.
Team onboarding cut to 1 day
Unlike the traditional developer onboarding process, which takes anywhere from several weeks to a month, Rocket Crew is deployed and integrated into processes within 24 hours. Onboarding happens only once: the system instantly learns the entire codebase, adopts the project’s conventions and patterns, and is ready to deliver first results on day one.
Lower development costs and unlimited scalability
Integrating Rocket Crew lets you fix production costs through a transparent subscription model instead of growing headcount and paying for workstations. The business can scale computing resources flexibly and without limits to match the workload, while keeping senior architects focused on complex creative and strategic challenges.
Rocket Context: a corporate knowledge management system
Problem: Project information is scattered: around 90% of key company decisions are made in chats, tasks are discussed on calls, and only a fraction of the context reaches task trackers. Nobody records agreements, and new hires don’t understand the project context. When priorities shift or newcomers are onboarded, reconstructing the logic behind decisions is almost impossible.
Solution: We built an AI-powered corporate memory platform. The system automatically analyses work communication from chats, calls, meetings and documents, extracts key decisions and stores them in a single knowledge base with tracking and search.
Use cases:
- CEO / Management: Tracking assignments, transparent decision-making and AI summaries of meetings.
- IT / Product: A history of decisions, automatic minutes of meetings (MoM) after calls and faster onboarding of new hires.
- HR / Recruiters: An internal knowledge base and an FAQ assistant.
Business impact:
50% less time spent searching for task information
The platform fundamentally changes how fast teams work with corporate information. With instant access to digitised project context, teams no longer spend hours hunting for links, documents and details that used to get lost in private messages. The system instantly extracts the essence of thousands of scattered messages and calls.
2x faster onboarding for new project members
The system enables seamless, self-guided onboarding. New members get up to speed much faster, as they can trace the full chain of decisions on their own through semantic AI search. Senior specialists no longer spend time on routine briefings and answering the same questions.
95% of critical project context retained when specialists rotate
The risk of knowledge loss (bus factor) is practically eliminated. When specialists rotate or key employees leave, the company reliably retains the full history of decisions, the reasoning behind them and all agreements, ensuring the security and continuity of core business processes.
Rocket Hunt: HR AI Screening
Problem: HR departments spend enormous resources manually screening thousands of irrelevant résumés. Slow processing means companies lose strong candidates in a competitive market.
Solution: Rocket Hunt is an intelligent hiring platform that turns an uncontrolled flow of applications into a transparent, manageable funnel. A Telegram bot collects candidate data, then the built-in AI instantly runs initial scoring and relevance assessment. Recruiters receive a pre-filtered candidate flow in a single workspace.
Use cases:
- HR / Recruiters: Automatic parsing of hundreds of résumés, instant scoring against job requirements and handling applications without manually sorting through email.
- Hiring Managers: Reviewing relevant candidates for their openings with an AI job-fit score, saving hours on technical screening.
- C-level / Executives: HR analytics, tracking time-to-fill and monitoring hiring funnel quality in real time.
Business impact:
30% shorter overall time-to-fill
Speed of response becomes the main competitive advantage. Automation instantly filters out irrelevant applications so recruiters can move straight to target candidates. This significantly shortens Time-to-Hire and keeps strong specialists from going to faster competitors.
40% of HR time freed up for complex cases
By delegating initial parsing to neural networks, HR specialists’ time is fundamentally reallocated. Hours once spent mechanically reading PDF files now go into in-depth interviews, headhunting rare specialists and building the employer brand.
20% higher conversion from application to first contact
The system assesses each application immediately, so the recruiter can reach a suitable candidate while their interest in the role is at its peak. Fast contact increases candidate loyalty, builds the image of a tech-driven company and raises the chances of a successful hire in a heated labour market.
Rocket Insight Lab: An automated research pipeline
Problem: Traditional user data collection takes too long, from weeks to months. Manual synthesis of dozens of interviews drags out product decision cycles.
Solution: We turned the research process into an end-to-end system that automates research using the Double Diamond method, taking a hypothesis from uncertainty to a finished concept:
- Stage 1 (Discover): The system provides smart onboarding and methodology selection. If the team is unsure which type of research is needed, they fill in a brief with the business task, and the algorithm recommends a format. Beyond interviews, it supports card sorting, competitive UX benchmarking and quantitative surveys.
- Stage 2 (Define): Recordings are automatically transcribed and synthesised into structured data (pain points, barriers, quotes). The process is many times faster thanks to a database of synthetic audience segments (AI personas) and a library of UI patterns from global market leaders.
- Stage 3 (Develop): A built-in fast report generator exports research results to a structured PDF document or presentation in one click.
- Stage 4 (Deliver): Depending on the insights, the system either suggests changes to an existing product or generates a concept for a new feature.
Use cases:
- Product Managers / CPO: Continuous validation of product hypotheses before development starts and fast data-informed insights for backlog prioritisation.
- UX/UI Researchers and Designers: Automated interview transcription and tagging, with ready-made lists of barriers linked to specific interface screens.
- Marketing / Growth teams: Testing value propositions and creatives on AI personas before launching large-scale campaigns on real traffic.
Business impact:
60% lower research costs
Rocket Insight Lab turns research from a slow, costly procedure into a continuous pipeline. Automated audio transcription and deep semantic synthesis of transcripts give teams structured insights instantly, skipping weeks of manual processing, tagging and listening to recordings.
10x less time spent on data extraction
Rocket Insight Lab turns research from a slow, costly procedure into a continuous pipeline. Automated audio transcription and deep semantic synthesis of transcripts give teams structured insights instantly, skipping weeks of manual processing, tagging and listening to recordings.
2x faster Time to Market
The pipeline’s main business outcome is the ability to ship well-founded features in the shortest possible time. Product teams stop relying on intuition and gain a tool for testing ideas continuously, protecting the company’s budget from investing in unwanted or flawed solutions.
End-to-end AI newsroom
Problem: Running corporate media and producing niche content takes a team of at least 10 specialists, so the return on that labour is slow. Quality content demands enormous effort, and either quality or publishing frequency suffers.
Solution: We built an autonomous AI system that generates expert content. The entire production chain, from finding a news hook to publishing on the Telegram channel and the website, is run by a multi-agent system. The human role comes down to one specialist who acts as editor-in-chief and validates the finished content stream.
Use cases:
- PR / Marketing: Non-stop management of corporate blogs and Telegram channels, fast newsjacking and automatic repackaging of releases.
- Editors-in-chief (content leads): Managing a pool of AI agents and shifting focus from routine writing to fact-checking and Tone of Voice control.
- Experts / internal speakers: Scaling their expertise without spending time: an expert simply records a thought as a voice message, and the system turns it into an in-depth column.
Business impact:
5x lower content production costs
An AI newsroom lets a company scale its media presence aggressively without expanding headcount. By replacing a pool of copywriters, rewriters and editors with a multi-agent system, the direct cost of producing articles, posts and news drops several times over.
70% faster content release
An autonomous production cycle removes the problem of lengthy approvals and drafting. Only minutes pass between a news hook appearing and a finished, verified piece being published. This lets the business lead the news agenda and be first to respond to trends.
3x more content produced
With just one editor validating the final output, the system generates a dense, continuous stream of expert content. The business can warm up its B2B audience systematically and at scale, covering every available distribution channel without losing quality.
What this gives us and our clients
Building this ecosystem at Rocket Tech let us shift our focus: we no longer spend time moving data from one window to another. We design the future for our clients while AI agents keep the knowledge base up to date, the content fresh and opportunities spotted on time.
We are ready to share this expertise. If you feel routine is eating away at your potential, it is time to audit your processes and build your own automated tools.
