AI & NLP Solutions

AI & NLP Solutions for Enterprise

AI systems that survive contact with real users: grounded in your data, evaluated continuously, and costed before they reach production.

Discuss your requirement See capabilities

What you get

RAG & knowledge assistants
NLP pipelines
Document intelligence
Conversational AI & agents

Overview

Why teams bring us in

The hard part of enterprise AI is not the model. It is retrieval quality, evaluation, guardrails, latency and the unit economics of every request. Demos hide all five.

Exubers builds the layers around the model: retrieval pipelines that surface the right context, evaluation harnesses that catch regressions before users do, and guardrails that keep outputs inside policy. We combine that with classical NLP — custom tokenisation, entity recognition and synonym mapping with spaCy — where it remains faster, cheaper and more predictable than a large model.

Capabilities

Inside our AI & NLP Solutions

Every engagement is scoped from this set. We do not sell all of it to everyone — we sell the parts that move your constraint.

RAG & knowledge assistants

Retrieval-augmented generation over your documents with citations, access control and answer-quality evaluation.

NLP pipelines

Entity recognition, classification, summarisation, custom tokenisation and domain-specific language models.

Document intelligence

Extraction and structuring of contracts, invoices, claims and forms with human-in-the-loop review where accuracy matters.

Conversational AI & agents

Assistants integrated with your systems, scoped to defensible tasks, with escalation paths and audit trails.

Evaluation & guardrails

Golden datasets, automated evaluation in CI, hallucination and safety checks, and prompt versioning.

Inference cost & latency

Model routing, caching, batching and fine-tuning decisions driven by measured cost per resolved request.

How we work

A delivery sequence you can plan around

Qualify

We test whether AI is the right tool for the use case, and define what "good" looks like as a measurable target.

Ground

Data preparation, chunking and retrieval design — the single biggest determinant of output quality.

Evaluate

A golden dataset and automated scoring, so every prompt or model change is measured rather than eyeballed.

Operate

Monitoring for drift, cost per request, latency and failure modes, with a rollback path for every model change.

GroundedAnswers cite source documents, not model memory

EvaluatedGolden-set scoring runs in CI on every change

CostedCost per resolved request measured before rollout

Technology

Tools we use in AI & NLP Solutions work

Chosen per engagement against your team's existing skills and constraints, never as a default.

OpenAI Anthropic Hugging Face LangChain LlamaIndex spaCy PyTorch pgvector Qdrant Milvus Ray FastAPI

FAQ

AI & NLP Solutions questions, answered

The questions procurement and engineering ask us most often before an engagement starts.

Ask us something else

How do you stop an enterprise AI assistant from hallucinating?

By grounding every answer in retrieved documents, requiring citations, constraining the model to refuse when retrieval confidence is low, and running an automated evaluation set on every prompt or model change. Hallucination is largely a retrieval and evaluation problem, not a model-selection problem.

Do you fine-tune models or use retrieval-augmented generation?

Retrieval first, almost always. RAG keeps answers current, is cheaper to maintain, and lets you update knowledge by updating documents. Fine-tuning earns its place for tone, structured output formats and narrow classification tasks — rarely for factual knowledge.

Can our data stay inside our own environment?

Yes. We deploy in your cloud account or on-premise, and can use self-hosted open-weight models where data residency or regulation requires it.

How do you control the running cost of an AI feature?

By measuring cost per resolved request rather than cost per token, then routing easy requests to smaller models, caching repeated retrievals, and setting hard budget alerts. Most AI features we review are paying frontier-model prices for work a small model does equally well.

What does a realistic first AI project look like?

A scoped internal use case with clear ground truth — search over policy documents, support-ticket triage, or document extraction. It proves the pipeline, evaluation and cost model on low-risk traffic before anything customer-facing goes live.

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