NLP & Conversational AI
Conversational AI covers assistants that handle queries through chat, and NLP covers systems that extract meaning from text — classification, entity extraction, sentiment, summarisation. We build both, measured on whether the user's problem was actually resolved rather than on whether they were prevented from reaching a human.
The problem
The same six questions
A large share of your volume is a handful of repeated questions
Where is my order, how do I reset this, what is the returns window. They are answered by the same people who handle the genuinely difficult cases, which means the difficult cases wait behind them.
The last bot made things worse and the team still flinches
It matched keywords, failed on anything phrased unusually, and looped. Customers who eventually reached a person had to start the whole story again, because nothing was passed across. The word "chatbot" now costs you political capital internally before any technical conversation starts.
Triage is a person reading the first line
Incoming requests land in a general queue and wait for someone to categorise them by hand. The mis-routed ones bounce between two teams before landing, and the time spent bouncing is invisible in every report you have.
What a real fix takes
Design the escalation before the conversation. A system measured on containment will learn to trap people — that is easy to build, it looks good on a dashboard, and it is expensive to recover from once customers have concluded your support is a wall. The number that matters is whether the problem was resolved, and a handoff to a person carrying full context counts as a success rather than a failure. The second unglamorous requirement is grounding: modern models are far less likely to misunderstand than the previous generation and far more likely to answer confidently from the wrong source, which is a harder failure to notice and a worse one to ship.
Outcomes
What changes
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
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 more forgiving of an imperfect first version.
Ticket classification and routing
that categorises and prioritises incoming volume, typically with immediate measurable effect on response time and usually the cheapest win available.
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.
Escalation and handoff engineering
The transfer path into your helpdesk, with context attached in the form an agent can actually read. Built first, and tested harder than the conversation itself.
What you receive
Three things
The intent map, drawn from your real history
What people actually ask, by volume, in their own phrasing, with what resolution genuinely requires for each — which system has to be touched, what a person needs to decide, where policy is ambiguous. It routinely reveals that the third-most-common request is something nobody had a name for. This is worth having even if you never build the assistant, because it is an accurate description of your support load that no workshop would have produced.
The escalation contract
The exact conditions under which the assistant stops and hands to a person, and what travels with the handoff: full conversation, what was attempted, what the customer already confirmed, and the reason for escalating. Written and agreed with the support team rather than decided in code, because they are the ones who inherit the consequences of getting it wrong.
The conversation review record
Sampled real conversations with what went wrong tagged by category, feeding a prioritised list of what to fix next. It is how scope gets widened on evidence, and it is the thing that stops the assistant silently degrading as your policies and products change.
How it works
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.
Ongoing
Conversation review, knowledge updates, scope expansion where the numbers justify it.
The pilot is where the surprises are, reliably. Real users ask things in ways nobody anticipated, arrive mid-problem with no context, and combine two unrelated requests in one message. This phase exists to absorb that, and shortening it is the most common false economy in this category.
Technology
What we work with
Technologies we work with, chosen per assistant rather than fixed in advance. Which channels are in scope depends on what your customers actually use and what access your own accounts already carry — we confirm that during scoping instead of assuming it.
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
Where this applies
Strongest where query volume is high and concentrated in a repeating set of questions, and where documentation exists to ground answers — retail and e-commerce, travel and hospitality, education and internal service desks are the clearest fits.
Weakest where every enquiry is genuinely unique, where the documentation to ground answers does not exist or contradicts itself, or where the emotional stakes are high enough that people want a person immediately regardless of capability. Bereavement, complaints and safeguarding conversations belong with humans, and we will say so.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
How we scope and price
Fixed scope, quoted after query analysis. Three things drive it.
How many intents are in scope
Each intent carries its own resolution path, edge cases and test set. Starting with the highest-volume handful is cheaper, ships faster, and produces the data that tells you what to add next.
How many systems the assistant must act through
Answering is cheap. Acting — issuing a refund, changing an address, checking an order — requires integration, permissions and reversibility per action.
The state of the underlying knowledge
Where documentation is current and consistent, grounding is straightforward. Where it is contradictory or missing, that has to be resolved first, and the assistant will otherwise surface the contradictions to your customers.
FAQ
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.
When your query volume is genuinely long-tail with no repeating pattern — the analysis will show this and the build will not pay back. When your documentation is thin or contradictory, in which case fixing it comes first, because the assistant will otherwise repeat the contradictions confidently. And when the conversations are emotionally difficult ones where a person is what the customer actually needs. We would rather establish this during query analysis than after launch.
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.
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