NLP & Conversational AI
Outcomes
Routine queries resolved immediately
Order status, policy questions, account actions, troubleshooting. Available continuously, answered consistently.
Clean escalation
The assistant recognises when it cannot help and hands over with full context, so the customer does not repeat themselves. This single behaviour determines whether people tolerate the system or resent it.
Unstructured text turned into data
Tickets, reviews, emails and survey responses classified, tagged and summarised at a volume no team can read.
Measurement that reflects reality
We track resolution and satisfaction, not containment. A bot that traps people is easy to build and actively damaging.
What we build
Customer-facing assistants grounded in your actual policies and documentation, capable of taking real actions through your systems rather than only answering questions.
Internal assistants for IT, HR and operations queries — often higher value than customer-facing deployments, because internal knowledge is scattered and the audience is forgiving.
Ticket classification and routing that categorises and prioritises incoming volume, typically with immediate measurable effect on response time.
Extraction systems pulling structured fields from unstructured text — contracts, emails, forms, clinical notes — with confidence scores and validation.
Summarisation for long threads, call transcripts and document sets, with the specificity that makes a summary useful rather than decorative.
Sentiment and theme analysis across feedback at scale, surfacing what changed rather than producing a static score.
How it works
Weeks 1–2 — Query analysis. We examine your real conversation and ticket history to establish what people actually ask, in what proportion, and what resolution requires. This defines scope far better than a workshop about what the assistant should do.
Weeks 2–3 — Scope and escalation design. Deciding explicitly what the assistant handles and what goes straight to a person. Narrow initial scope with clean escalation outperforms broad scope with poor handoff, consistently.
Weeks 3–6 — Build. Conversation design, knowledge grounding, system integrations for actions, guardrails, escalation logic.
Weeks 6–8 — Evaluation and tuning. Tested against real historical queries with known good outcomes. Failure modes catalogued and addressed.
Weeks 8–10 — Pilot. Live on a subset of traffic with monitoring and rapid iteration. Real users ask things nobody anticipated; this phase exists to absorb that.
Ongoing. Conversation review, knowledge updates, scope expansion where the numbers justify it.
Technology
Models: Claude, GPT and Gemini for conversation; smaller and open-weight models for classification and extraction where they match performance at lower cost.
Grounding: retrieval over your documentation and policies, so answers reflect current content rather than training data.
Integration: your CRM, ticketing, order and account systems — so the assistant can act, not just talk.
Channels: web, WhatsApp, Slack, Teams, and existing support platforms.
Evaluation: resolution rate, escalation quality, satisfaction, and per-intent accuracy against a held-out set of real queries.
Where this applies
Strongest where query volume is high and concentrated in a repeating set of questions, and where documentation exists to ground answers.
Weakest where every enquiry is unique, or where the emotional stakes are high enough that people want a person immediately regardless of capability.
How we scope and price
Fixed scope, quoted after query analysis. Cost is driven by how many intents are in scope, how many systems the assistant must act through, and the quality of the underlying knowledge base. We usually recommend starting narrow — the highest-volume intents only — because it is cheaper, ships faster, and produces the data that tells you what to add next.
Frequently asked questions
Older systems matched fixed intents and failed on any phrasing outside their script. Language models interpret meaning, so they handle variation. The failure mode has changed too: modern assistants are less likely to misunderstand and more likely to answer confidently from the wrong source, which is why grounding and evaluation matter.
Depends entirely on your query mix, which is why we analyse it first. Concentrated volume in a few repeating intents produces high resolution; long-tail variety produces less. We estimate from your actual history rather than quoting an industry figure.
It will if it cannot escalate. We design the handoff first and treat escalation as a success path rather than a failure, and we measure satisfaction rather than deflection.
It can act — issue a refund, update an address, check an order — through your systems, with permissions scoped per action and approval gates where the action carries risk.
Grounding in your actual documentation with citation, explicit refusal when the answer is not covered, evaluation against real queries, and conversation review after launch. Reduced and measured, not eliminated.
Modern models handle major languages well and less-resourced languages variably. We test against your actual customer languages rather than assuming.
More AI services
AI Strategy Consulting
Turn scattered AI ambition into a sequenced, costed plan. We decide what to build, what to buy, what to ignore, and in what order.
AI Readiness Audit
A 3–4 week assessment of your data, systems and processes that returns a ranked, costed list of AI use cases and an honest verdict on what you can deploy now.
Agentic AI Automation
We build AI agents that complete multi-step work inside your systems — with defined scope, human checkpoints, and evaluation. Deployed to production, not demos.