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Intelligent escalation
The AI recognises when a conversation requires human judgement or when a caller simply asks to speak to a person.
Adhki combines Agentic AI Voice Agents with human expertise to handle high volume customer interactions across voice, chat and email.
Our AI agents understand intent, reason through what needs to happen next and take action across your systems to resolve conversations end to end. And when AI shouldn’t handle it, it doesn’t. The conversation is seamlessly handed to a live human agent, with the full context, ready to take over.
AI when it works. A real person when it matters.
1.8 billion interactions a year · enterprise median go live 9 weeks
Your team builds the knowledge bank policies, price lists, contracts, SOPs, ticket history, wherever the answer lives. The agent reads across all of it, reconciles what it finds, and cites the source. The same lookup by hand usually means opening two or three systems.
Most conversations are handled from start to finish by Adhki AI agents. But when a caller requests a person, the conversation becomes complex, or the AI determines a human is the better option, the call is hot transferred to a live agent without the caller needing to repeat themselves.
The AI recognises when a conversation requires human judgement or when a caller simply asks to speak to a person.
The call is transferred live to a trained UK based agent with the full conversation context, so the discussion continues naturally.
The caller does not start again and does not repeat themselves. The handover from AI to human happens inside the same call.
Traditional quality assurance listens to one or two per cent of calls and extrapolates. At 50,000 contacts a month, that is a few hundred calls being used to manage a whole workforce. QIP evaluates every contact you handle, human and AI, against your own rubric, explains each score, and turns the result into a coaching plan rather than a spreadsheet.
Three agents flagged on the same behaviour: objection handling on first refusal. Two teachable clips attached per agent, drawn from their own calls. Coach the coach view shows whether last month’s plan actually shifted the score.
Bespoke models calibrated to your scorecard and your tone of voice. Compliance lines, process adherence, empathy, effort and outcome achievement, each with the evidence quoted from the transcript.
Adhki correlates every behaviour to the outcomes you care about. Resolution, CSAT, conversion, collections yield and shows you which criteria on your scorecard actually predict them, and which are noise.
Scores become a ranked list of who to coach and what to coach on, with clips from that agent’s own conversations. Leaders see whether coaching actually moved the number.
The same conversation understanding that scores a call afterwards drives guidance during it, next best action, entitlement limits, the compliance phrase that has not yet been said.
Mandatory disclosures, vulnerability signals, complaint markers and prohibited claims flagged on every contact, with an immutable evidence trail your regulator and your risk team can both work from.
Synthetic customers built from your own hardest calls let new starters and existing agents rehearse against realistic difficulty before they take a live contact and be scored on the same rubric.
Most operational questions are answerable from the conversations themselves, but the volume makes them impractical to read. Call Analyser reads across the corpus, answers in plain English, and cites the conversations it drew from so you can check the working.
A replay of each step: the agent reads the account, calls the tool, writes the record and confirms, in the channel the customer used. Tool calls are shown in green.
Enterprise contact operations don’t get fixed by automation alone. The contacts a machine should never take still need to be handled well, and every contact, machine or human, needs to be measured. Adhki does all three on one platform, with one shared understanding of the conversation.
AI agents that take the conversation from first contact to closed record — reading the account, calling the tool, writing the outcome back. Voice, chat, email and messaging from a single agent definition.
See a worked example →When a person takes over, they get live guidance grounded in the same knowledge the agent used: the next best action, the entitlement they can offer, the compliance line they must say, and the wrap up written for them.
How handover works →Every conversation on both sides scored against your rubric, correlated to the outcomes you measure, and open to plain English questions across the whole contact history.
Explore QIP →No exporting to a second tool between stages. Each stage feeds the next: test failures become training cases, live transcripts become knowledge articles.
Write the job in plain language. Studio drafts the flow, the tool calls, the fallbacks and the tone, then shows you exactly what it assumed.
Run thousands of simulated conversations. Adversarial, multilingual, interrupted and get a pass/fail report before anyone goes live.
Ship to every channel from one definition. Roll out by percentage, region or intent, and roll back in a single click.
Copilot clusters failures, drafts the missing knowledge article and proposes the fix. You approve; the agent ships it.
Natural language authoring with a visual canvas underneath. Non engineers ship changes; engineers keep version control, diffs and CI.
Explore Studio →Agentic retrieval that plans its own lookups across your website, PDFs, ticket history and live APIs and cites exactly what it used.
See retrieval traces →Every turn is replayable: the prompt, the model, the retrieved chunk, the tool payload, the cost. You can see what happened without reproducing it.
Open a trace →Golden conversations, adversarial suites, PII leak checks and grounding scores run on every change, so a prompt edit can't break a refund path without anyone noticing.
See a test report →When judgement is needed or a caller simply asks for a person, the conversation hot transfers to a live UK agent with the full context already in front of them.
See how handover works →Running every step through one large model means paying frontier prices for simple classification and accepting frontier latency on routine questions. Adhki routes each step to a model chosen on quality, cost and speed, and lets you swap in a newer one without editing a flow.
"Agentic" is used to mean several different things. This is how the common approaches compare on the points that tend to decide an enterprise evaluation. Worth putting the same questions to every vendor on your shortlist.
| Capability | Rules based chatbot platforms | Single model agent tools | Adhki |
|---|---|---|---|
| Model choice | N/A — intent trees | One vendor, one price curve | 20+ models, routed per step |
| Completes actions in your systems | Handoff to a human | Limited, per integration | Read + write across 180+ connectors |
| Context across channels | Session resets | Separate bot per channel | One memory, any channel |
| Pre launch testing | Manual QA | Prompt eyeballing | Automated regression + adversarial suites |
| Explainability | Flow logs | Raw transcripts | Full decision trace per turn |
| Deployment | Vendor cloud only | Vendor cloud only | SaaS, private cloud or in VPC |
| Pricing model | Per session | Per token, marked up | Per resolution — you pay for outcomes |
Each sector pack ships with domain intents, compliance guardrails, reference integrations, a scoring rubric and an evaluation set so the first build starts from a working enterprise baseline rather than an empty project.
Card disputes, KYC refresh, arrears and payment plans with step up authentication and an immutable audit trail on every decision.
−63% handle timeFNOL capture, policy servicing, renewals and claim status across the book, with regulator facing evidence produced automatically.
2.4M claims / yrOrder status, returns, exchanges and delivery exceptions written straight into the OMS, including Black Friday and post peak surges.
11× peak loadOutage reporting, meter readings, billing disputes and payment arrangements that hold up when a storm puts six months of volume into one week.
92% containmentActivations, plan changes, faults, roaming and retention across consumer and business bases running into the millions of subscribers.
1M+ contacts / moDisruption rebooking, refunds, ancillaries and duty of care contact at the exact moment demand spikes hardest.
8× disruption day surgeBanks, insurers, carriers, utilities and national retailers, every one of them handling more than 50,000 interactions a month. Some run fully autonomous, some hand to a person by design, each card says which, why, and at what volume.
Four enterprise programmes, each above 200,000 contacts a month, and what changed in the first year. Full case studies available under NDA.
Each figure is customer reported and measured against that customer's own prior baseline. Baselines differ, so the numbers aren't directly comparable between organisations.
Move the sliders. Cost per contact is derived from your fully loaded agent hourly rate divided by how many contacts an agent resolves in an hour. Resolution rates are assumed to reach your target over the first two quarters, which is typical at enterprise volume. It's an estimate, not a quote.
Net of platform cost, based on the inputs you set. Your actual figures will depend on contact mix and how much of it is automatable.
Security review is usually the longest step in a deployment. These are the controls in place and the evidence available, so your risk team can start reviewing on day one.
Answers must resolve to a cited source or the agent escalates. No source, no claim.
Sensitive data is masked before it reaches any model, and never used for training.
Pin processing to the UK, EU, US, India, UAE or Australia — or run entirely inside your VPC.
High impact actions like refunds or account changes require the policy you define.
Every decision, model, prompt and tool call retained and exportable to your SIEM.
180+ maintained connectors across CRM, service desk, telephony, commerce and identity, plus a typed SDK and MCP support for anything not on the list.
Our deployments start around 50,000 interactions a month and run past a million. Below that the integration and assurance work rarely pays for itself inside a year. Above it, the case is usually made on quality intelligence alone, scoring 100% of contacts instead of a 2% sample, before automation is counted at all.
No. Adhki runs on your existing telephony and sits alongside Genesys, NICE, Amazon Connect, Zendesk or ServiceNow. Most enterprise programmes start as an overflow or after hours layer on one queue, prove the numbers, then expand line of business by line of business.
It varies by step. Frontier models for multi step reasoning, small fast models for classification and safety screening, open weight models for retrieval, and your own fine tunes for policy lookups. You can pin a model, exclude one, or bring your own keys.
It hot transfers to a trained UK based agent with the full transcript, the customer record and a summary of what it already tried, so the person picks up mid conversation rather than starting over. That handover is scored in QIP like any other contact.
Per resolution, not per token or per session. If the agent doesn't resolve the contact, you don't pay for it. Quality intelligence is licensed separately per scored contact. Committed use pricing applies above 250,000 contacts a month.
Nine weeks is our median from kickoff to production on a first line of business, including security review and procurement. QIP and Call Analyser can go live sooner, since read only analysis of your existing recordings needs no changes to call flow at all.
Bring one real queue and a week of recordings. We'll build a working agent on your own data during the session and score a sample of your existing calls in front of you. Thirty minutes.