Capacity you can’t hire fast enough
Agents absorb high-volume, rule-heavy work — first-line triage, document handling, moving data between systems — so your people spend their hours on the exceptions and judgement calls that genuinely need a person.
AI agents for real business operations
Put AI agents to work.
Keep control.
Keen Agents helps companies identify the right use cases, prove their business value, deploy reliable AI agents into production, and operate them as part of the business.
Trusted by operators in logistics, distribution & retail
The executive decision
Three questions management has to answer.
Before a single prompt is written, an AI agent project lives or dies on three business questions. We help you answer them honestly.
Where is the value?
Identify the process and the business metric that actually moves — before anyone talks about models or frameworks.
Will it reach production?
Design for exceptions, monitoring, integration and ownership — not just a convincing demo that stalls at pilot.
How do we keep control?
Preserve flexibility across models, cloud, data and vendors, with humans in the loop where it counts.
Business outcomes
What that delivers.
Not features — outcomes an operator can feel. Here is where agents earn their place in the business.
Consistent service, around the clock
Standard requests are answered the same way at 2 a.m. as at 2 p.m. — with a clear, logged path to a human the moment something is out of scope.
Steady under peak load
Throughput scales up and down with demand instead of a hiring scramble — and performance holds when volume spikes.
How it works
A clear path to production, phase by phase.
A structured four-phase framework that moves the right use case from discovery to production — each phase bounded, measured, and reversible.
01
Discovery
Map the highest-value use case
1–2 weeksWe audit your workflows, data and systems to pinpoint where an agent creates real leverage — and we agree the success metric up front.
Use-case auditData readiness reviewSuccess metric & target
02
Agent & operating-model design
Architect the agent, its tools and its guardrails
2–4 weeksWe design the agent, choose the models, wire the integrations, and define who owns exceptions — before anything ships.
Agent architectureTool & model selectionGuardrails & escalation design
03
Integration & controlled pilot
Deploy into a real pilot with live data
2–6 weeksWe run the agent against live data in a bounded pilot, with telemetry, logging and human escalation from the very first request.
Pilot deploymentMonitoring dashboardPilot report & scale / stop decision
04
Production, monitoring & scale
Operate, watch and keep improving
OngoingWe operate the agents in production, watch the metrics and keep improving — expanding scope as trust and results compound.
Production operationsPerformance monitoringRoadmap & scale-up
Production & control
What executives need to know about the agent layer.
The technical layer matters because of the business risk it removes. Here is the plain-English version.
Reliable under real inputs
The agent is built and tested against the messy, long-tail inputs production actually sends — not a curated demo script.
- The risk it removes
- Removes the risk of a system that dazzles in a pilot and breaks on a real Tuesday afternoon.
- How we prove it
- Evaluated continuously against real cases, before and after launch.
admin.keenagents.ai / statistics
Under the hoodCustom LLM engineIn-house tool nodesGCP + KubernetesAgent-to-agent messaging
Customer stories
Operators putting agents to real work.
A selection of the teams we work with — named sectors and real workflows. We show the work, not invented numbers.
Keos Bulgaria
Automotive & Distribution
Unimasters
Logistics & Supply Chain
UchMag
Educational Products & Retail
“Keen Agents streamlined our distribution workflows and gave us clear visibility into our operations. The implementation was fast and the team was highly responsive.”
MT
Management Team
Keos Bulgaria · Automotive & Distribution
“The AI-powered automation reduced our response times and helped our support team focus on the complex cases. A real step-change for our logistics operations.”
OT
Operations Team
Unimasters · Logistics & Supply Chain
“Working with Keen Agents changed how we handle our product catalogue and customer inquiries. The integration with our existing systems was seamless.”
MT
Management Team
UchMag · Educational Products & Retail
Insights
Field notes for AI decisions.
Clear thinking for the people who approve, own and operate AI agent projects — written without hype or invented numbers.
Where AI Agents Create Value in Operations — and Where They Do Not
A practical map of the operational work where AI agents earn their keep — and the work where they quietly destroy value.
12 June 2026 · 7 min
When an Agent Is the Wrong Solution
A buyer's guide to telling genuine agent problems from work that a script, a form fix, a policy change, or a hire would solve faster and cheaper.
3 June 2026 · 6 min
How to Measure the Economics of an AI Agent
A CFO-grade framework for costing an AI agent by the fully-loaded price of a resolved task — not by the token — measured against the baseline you already run.
22 May 2026 · 6 min
Human Oversight Without the Bottleneck
Keeping people in control of agents does not mean putting a person behind every decision — it means designing where judgement is spent.
14 May 2026 · 6 min
About us
We fix what stalls AI projects.
Most AI projects never reach production.
Not because the technology isn’t good — but because execution is hard, tools are fragmented, and teams lack the right mix of AI and engineering.
We built a company that fixes that. Senior engineers and AI-native talent. Fast pilots, reliable systems, real results.
Leadership team
Petar Denev
Co-founder & CEO
Victor Valtchev
Co-founder & CTO
As founders, we could sing you the song of how smart we are and how much experience we have. Yes, we have experience — quite a lot, honestly. But AI agents are a dynamic, still largely unknown space, and there is no point pretending anyone has it all figured out. So instead of bragging, we bring a humble, well-intentioned and bold approach. No AI theatre, no bullshit. About our team, though — that we should brag about. We have brilliant engineers and genuinely AI-native people we’re proud of.
FAQ
Answers for the people signing off.
The questions owners and C-level leaders actually ask — about control, risk, integration and what happens after launch.
How do you keep our data secure and under our control?
Your data stays within your governance. We can work inside your cloud or ours, apply encryption in transit and at rest, role-based access and audit logging, and isolate tenants in multi-client environments. We do not use your data to train third-party models.
How do you manage errors, exceptions and edge cases?
Every agent has confidence thresholds and a defined escalation path: low-confidence or out-of-scope cases route to a named human or queue, with a full audit trail. High-risk actions are contained so that a mistake is recoverable rather than irreversible.
How much of the process stays under human control?
As much as you decide. We design the operating model with you — from a light audit-and-sample review through to full human sign-off on sensitive actions — and we make the boundary explicit rather than assumed.
Will this require major changes to our IT or processes?
Usually not. Agents connect to your systems over APIs and lightweight connectors and fit around existing workflows. During discovery we produce an integration view so the effort is clear before you commit.
How do you avoid vendor and model lock-in?
Our engine is model-agnostic: it can route to any major model, so you can switch as prices, terms and capabilities change. Your process logic and your data stay yours — not trapped inside a single provider.
How do we decide whether the pilot should scale?
The pilot is a bounded experiment with agreed success criteria and a stop condition set in advance. Its report ties the results to the metric we defined in discovery, so the scale-or-stop decision is evidence-based, not a matter of momentum.
Who operates and improves the agent after launch?
Your choice. We can hand over runbooks and an operating model for your team, or continue to operate and optimise the agents on your behalf under an agreed support arrangement.
What does our team need to provide?
Access to the people who understand the process, visibility of the systems involved, and a decision-maker to approve the process map and pilot acceptance. The engineering and platform work is ours.
Still deciding?
Book a consultation and we'll talk through your specific process — honestly, no slide-ware.