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AI Agents that solve real operational problems

Below are example deployments and typical results — patterns drawn from real workflows, anonymised for privacy.

Examples below are anonymised; metrics shown are typical ranges and depend on workflow complexity.

EU-based team in Tallinn, Estonia — working with clients worldwide.

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HomeUse Cases & Results

Typical results across deployments

Ranges based on observed patterns. Final results depend on workflow complexity, data quality, and volume.

35–55%

Avg. ticket deflection

typical range; depends on query volume and knowledge base quality

< 1 min

Time-to-first-response

typical range for automated handling

+10–25%

Lead → meeting booked

typical uplift; varies by ICP definition and volume

20–40%

Ops cycle time reduced

typical range for approval and onboarding workflows

Metrics are indicative ranges observed across similar deployments and should not be taken as guarantees.

Example deployments

Real problems, real patterns — anonymised for privacy.

Support

High-volume FAQ + repetitive tickets

The problem

Support teams spend 60–70% of their effort answering the same 20 questions, leaving complex tickets under-served and response times high.

Agent solution

  • Agent ingests knowledge base and resolves common queries instantly
  • Unresolved cases escalate to a human with full conversation context attached
  • Every interaction is logged for quality review and KPI tracking
ZendeskIntercomSlackEmail

Before → After

First-response time2–8 hours< 2 minutes
Tickets resolved without human0%35–55%
Handle time (escalations only)baselinereduced 20–35%

What changed

The support team now handles only the edge cases that genuinely need human judgment — with full conversation history already attached. Routine volume runs automatically.

Support

Order status requests + escalation routing

The problem

High volumes of 'where is my order?' queries create queue backlogs and delay resolution of the disputes that actually matter.

Agent solution

  • Agent resolves order-status queries by calling backend or CRM APIs directly
  • Billing disputes and delivery failures escalate with structured summaries
  • After-hours queries are handled without additional staffing
ZendeskEmailSlack

Before → After

Status query resolution timehours< 3 minutes
After-hours unresolved rate100%~30–50%
Escalation misroutingfrequentnear-zero

What changed

Routine status queries are resolved around the clock. Escalations arrive with structured summaries so agents resolve them in one touch rather than several back-and-forth exchanges.

Sales

Inbound lead qualification & routing

The problem

Sales reps spend hours on discovery calls with poor-fit leads, while high-intent inbound prospects wait too long for a first response.

Agent solution

  • Agent scores inbound leads against defined ICP criteria automatically
  • Qualified leads get routed to the right rep and a discovery call booked instantly
  • Unqualified leads enter a nurture sequence without manual intervention
HubSpotSalesforceGoogle CalendarSlack

Before → After

Time-to-first-response2–4 hours avg.< 5 minutes
Qualified meetings booked / weekbaseline+10–25%
Rep time on unqualified callshighsignificantly reduced

What changed

Qualified leads get a meeting booked within minutes of enquiring. Reps spend time on conversations that are actually likely to convert. CRM records are updated throughout, automatically.

Sales

Follow-up summaries + CRM hygiene

The problem

Reps forget to log calls, deal stages go stale, and follow-up timing depends on individual discipline rather than a reliable system.

Agent solution

  • Agent summarises call notes and writes structured CRM updates automatically
  • Sends follow-up reminders to reps and scheduled messages to prospects
  • Flags stale deals and prompts next-step action
HubSpotSalesforceSlackGoogle Calendar

Before → After

CRM record completeness40–60%85–95%
Follow-up delay after call1–3 dayssame day
Deal stage accuracyinconsistentconsistently updated

What changed

CRM hygiene becomes a system property, not a discipline problem. Reps get clear next-step prompts; managers get accurate pipeline data without chasing updates.

Ops

Employee onboarding requests

The problem

New-hire onboarding involves repetitive manual steps across HR, IT, and management — causing delays, inconsistency, and a poor day-one experience.

Agent solution

  • Agent triggers onboarding checklists automatically on the start date
  • Handles access requests, tool-provisioning prompts, and welcome sequences
  • Tracks completion and escalates blockers to the right person
SlackNotionGoogle WorkspaceZapier

Before → After

Time to complete onboarding checklist3–7 days1–2 days
Manual HR touchpoints per hire8–122–4
Access provisioning delaysfrequentnear-eliminated

What changed

Onboarding becomes a consistent, trackable process rather than a checklist that depends on HR availability. New hires get timely access, instructions, and check-ins from day one.

Ops

Internal approvals & task routing

The problem

Approval requests get lost in email threads and Slack DMs, creating bottlenecks and leaving no clear audit trail for compliance or accountability.

Agent solution

  • Agent receives approval requests via Slack or email and routes to the correct approver
  • Sends reminders, tracks decisions, and logs outcomes with timestamps
  • Escalates overdue items automatically to the next approver level
SlackEmailNotionJira

Before → After

Avg. approval turnaround2–5 dayssame or next day
Requests with no audit trailmostnear-zero
Escalation visibilitylowclear, timestamped trail

What changed

Approval workflows stop relying on people remembering to follow up. Every request has a clear owner, status, and history — visible to anyone who needs to see it.

How we measure ROI

We agree on KPIs before any work begins. Here's the framework we use across all deployments.

Deflection rate

Percentage of queries fully resolved by the agent without human involvement. Primary metric for Support agents.

Escalation rate

Proportion of interactions handed off to a human. Lower is generally better; we also track escalation quality and context completeness.

Time to first response

How quickly the agent acknowledges and begins processing a request — measured against the pre-deployment baseline.

Handle time

Average time from first contact to resolution. Tracked separately for agent-handled and human-escalated cases.

Lead-to-meeting conversion

Percentage of inbound leads that result in a booked discovery call. Measured before and after deployment for direct comparison.

Ops cycle time

End-to-end time for a recurring operational process — approval, onboarding, report cycle — compared to the pre-agent baseline.

What's included in every build

Regardless of which agent you choose, the delivery process is always structured, transparent, and documented.

Discovery + KPI definition

We define what success looks like in measurable terms before any work begins.

Knowledge base ingestion

Your docs, FAQs, and structured data are prepared for RAG retrieval or structured content lookup.

Guardrails + human escalation

Confidence thresholds and escalation rules ensure uncertain cases always reach a human with full context.

Integrations + audit logs

Every action the agent takes is logged. Every integration is documented and reversible.

Testing + iteration

We test against real edge cases before launch and iterate based on your review.

Monitoring + ongoing improvements

Post-launch performance tracking with monthly reviews and continuous optimization.

Frequently asked questions

Still have questions? Email us at contact@flowbook.ee

Most MVP agents are live within 7–14 business days for standard workflows. More complex builds with multiple integrations or custom tools may take 3–4 weeks. We provide a clear timeline at scoping — before any work begins.

We need API or integration access to the tools the agent will interact with, plus any existing documentation (FAQs, process guides, knowledge base content). The discovery session takes 60–90 minutes. Your team reviews and approves all logic before we build.

Pricing is split into a one-time setup fee (scoping, build, integration, testing) and a monthly retainer (monitoring, optimization, support). Both are fixed-scope and agreed in writing before work begins. Final pricing depends on the number of integrations, workflows, and action volume.

We are based in Tallinn, Estonia and operate under EU privacy standards. Data flows are documented, encrypted in transit and at rest, and we can sign a Data Processing Agreement (DPA) on request. We do not use your data to train any AI models.

Yes. Most clients start with a single, well-defined workflow — a pilot that proves value before expanding. This limits risk and gives your team time to see how the agent performs in a real environment before committing to more.

We do, as part of the monthly retainer. This includes performance monitoring, prompt adjustments, integration updates, and periodic reviews based on your KPIs. You will never be handed a black box and left alone.

The agent escalates to a human via your chosen channel (Slack, helpdesk ticket, email) with full conversation context attached. You define confidence thresholds and escalation routing during setup. Nothing gets silently dropped.

You do. Every agent we build ships with full documentation — workflow maps, prompt logic, integration notes, and handover materials. If you want to bring it in-house or switch providers, everything is documented and transferable.

Ready to stop doing work that should be automated?

Tell us about one workflow. We'll suggest how an agent can handle it, give you a scope, and show you what measurable outcomes to expect.

Book a call

Get a proposed scope in 24–48 hours.

Get an AI audit

We'll review one workflow and suggest an agent-based solution.

Prefer email? contact@flowbook.ee