45%+
of incoming customer queries now resolved by AI agents without human handoff, on average.
Industry data, 2025
Intelligent Automation
AI agents that resolve, not just escalate.
Chat agents that actually resolve customer issues, handle internal requests, and integrate with your CRM, knowledge base and ticketing. With retrieval from your real documents, evaluation and a graceful handoff to humans when the agent is unsure.
Agents grounded in your data, not generic chatbots.
The old chatbot era was a queue with a friendly UI: collect the question, pass it to a human, claim deflection. The new conversational AI actually answers, when it knows the answer, and only then. We build agents that resolve issues, not just pass them along. Retrieval against your real knowledge base. Confidence-based handoff. Audit trails for every decision. Integration with the CRM, ticketing and ops systems where the resolution actually lives. And continuous evaluation that catches quality changes before customers notice.
Where conversational AI lands first
High-volume customer interactions where the cost of waiting is real, and where good answers can be grounded in data you already have.
Retail & e-commerce
- Order, refund and return self-service
- Product discovery and recommendation assistants
- Sizing, fit and availability questions
- Loyalty and rewards account help
Why Codino
- Grounded in your data. Agents answer from your documents, not from generic training data.
- One team owns the whole stack. Design, retrieval indices, integrations, evaluation and monitoring.
- EU-based with EU data residency and GDPR-compliant delivery by default.
- Evaluation-first. Golden datasets, live monitoring and clear handoff rules from day one.
- Human-in-the-loop until proven. Review queues until the data supports full autonomy.
- We design our exit. Your team operates the agent the day we leave.
By the numbers
Conversational AI is delivering, and the gap between leaders and average is large.
Self-service rates separate the AI agents that resolve from the ones that just escalate. Where the data is good and the integration is real, the difference is dramatic.
80%
of routine queries resolved by AI agents in well-built deployments.
Industry data, 2025
60×
faster first-response time reported in retail AI agent deployments, from 12 min to 12 sec.
Freshworks case data, 2025
$49.8B
projected conversational AI market by 2031, up from $17B in 2025.
Market data, 2025
Calculate the impact
Plug in your ticket volume, see the savings.
A rough but honest estimate of what conversational AI could save in your support operation. Drag the sliders to your situation. The math and assumptions are listed below.
Monthly savings
$47,000
Annual savings
$564,000
FTE-equivalents freed
8.5
Assumptions and methodology
- Automated cost per resolved ticket set at $0.30, the typical AI-plus-retrieval inference cost for a resolved conversation.
- Self-service rate is the % of tickets the agent resolves on its own; the remainder transfers to humans with full context.
- FTE-equivalent uses a fully-loaded support-agent cost of $5,500 per FTE-month (≈ $66k annually).
- Excludes one-time delivery cost, knowledge-base preparation and CRM integration. These are scoped per engagement.
- Savings are gross. We help you measure realised net savings during phased rollout.
What you get
A complete conversational AI stack: agents, document retrieval, integrations, evaluation and monitoring. Built to actually resolve, not just escalate.
Customer-facing chat agents
Resolution-grade agents on web, mobile, WhatsApp and SMS. Grounded in your knowledge base, integrated with your CRM and ticketing.
Internal help-desk and ops assistants
IT, HR and ops self-service. Knowledge-base Q&A, workflow triggers, ticket triage, with the same evaluation discipline as external agents.
Document retrieval grounded in your data
Search infrastructure, hybrid retrieval, freshness handling, document ingestion pipelines. The agent answers from your docs, not from training data.
Confidence-based handoff to humans
Clear thresholds for when the agent answers, when it asks a clarifying question, and when it transfers to a human. The human gets full context, the customer doesn't repeat themselves.
Evaluation that catches quality changes
Golden conversation sets, automated grading, regression tests and live monitoring. When resolution rate or CSAT moves, you know why.
Compliance-aware design from day one
PII handling, age and identity verification, audit trails, jurisdiction-aware policies. Built in, not bolted on.
Selected work
Recent projects where conversational AI replaced support queues with actual resolution.
Insights
Web-based tool that simplifies and accelerates airline research and analysis activities. Additionally, its risk tracking capabilities simplify risk management, auditing and collaboration in order to find additional value for the airline.
View Case Study
ZipZero
ZipZero lets users collect funds from their purchases to pay monthly household bills. Backed by AI that scans receipts and recognises products, it lets users earn from their own shopping data.
View Case StudyWhat Clients say about us
Marcin Walaszczyk
CTO
"The expertise of the leaders, coupled with the diverse skill sets of their teams, truly sets them apart. Their vast experience across a myriad of projects ensures that they can adeptly handle virtually any project you envision. Furthermore, their deep involvement in the process is palpable; it's as if they seamlessly integrate and become an intrinsic part of your in-house development team."
ZipZero
How we deliver
From customer-journey audit to a resolving agent. Usually 6 to 10 weeks for the first production rollout.
- 01
Discover
Customer-journey workshop, conversation log analysis, knowledge-base audit. We identify the top resolvable intents and the data gaps blocking them.
- 02
Prototype
Narrow agent on the top 10–20 intents. Retrieval over real docs, real conversations from your channels, confidence-based handoff. You see resolution numbers on real traffic in weeks.
- 03
Productionise
Evaluation harness, monitoring, audit trails, compliance review, CRM and ticketing integration. The agent runs on a slice of real traffic, measured against CSAT and resolution rate.
- 04
Scale
Expand intent coverage, ingest agent corrections into the eval loop. Resolution rate grows as the evidence supports it.
What changes when agents resolve
Schedule an agent workshopSupport volume drops without dropping CSAT
Routine resolutions handled in seconds. Your human agents handle the conversations that need judgement, and have time to do them well.
Customer wait time stops being a leadership topic
First-response time and time-to-resolution move measurably in the right direction. Customers notice.
Knowledge gaps surface, instead of hiding in tickets
Where the agent escalates, you see exactly which intents need better data, better policy or better workflow.
Compliance reviews pass without re-engineering
Audit trails, PII handling and jurisdiction rules are part of the design, not retrofitted under regulator pressure.
How we engage
Our way of working with you
The principles we apply on every engagement, independent of channel, model or scope.
- 01Listen first
We start with your customer, not our agent framework.
A workshop with your support and ops teams. Real conversations, real intents, real failure modes. You leave with a prioritised intent backlog.
- 02Define what good means
Resolution rate and CSAT are measurable before launch.
We co-define target resolution rate per intent, CSAT thresholds and escalation rules. So at handoff we can prove value, not argue about it.
- 03Embed, don't silo
Senior engineers alongside your team.
We work next to your support, ops and engineering teams. The agent reflects the resolution paths they actually have.
- 04Ship in weeks
A narrow first agent on real traffic fast.
First intent set fully wired against real conversations, typically in 4 to 6 weeks. You see whether the value is real before scaling.
- 05Design our exit
Your team owns it the day we leave.
Prompt libraries, document ingestion pipelines, eval harness and monitoring. Built so your team operates the agent without us.
Conversational AI, explained
Traditional chatbots run on intent trees and keyword matching. They break the moment a user phrases something unusually. Conversational AI uses large language models and document retrieval to understand intent and ground answers in your data. The result is actual resolution, not just routing.
Yes, web chat, mobile apps, WhatsApp, SMS and embedded messaging. We integrate with the contact-centre and messaging platforms you already use.
Retrieval-grounded answers with citations, confidence thresholds, refusal patterns where the agent isn't sure, and a strict scope on what the agent will speak to. Every answer is traceable back to the source document.
PII detection and redaction in conversation logs, EU-region deployments, audit trails for every action, jurisdiction-aware policies. GDPR-compliant by default, not retrofitted.
Most deployments hit measurable self-service resolution within the first 4 to 6 weeks on a narrow intent set. Numbers grow as we expand intent coverage and feed corrections back into the eval loop. We publish the improvement curve, honestly.
Let's Talk About Your Project
Get In Touch
Maciej Roman|CEO & Co-founder