Services Data and Artificial Intelligence
icitech partners with organisations that want to move enterprise LLM, AI assistant, and generative AI applications from proof of concept into production — supporting architecture selection, knowledge access, evaluation, and integration throughout the full engagement.
01 Context
A generative AI proof of concept can be made to function in a matter of days. The real challenge is connecting that application reliably to authorised organisational knowledge, ensuring it behaves according to user permissions, managing the risk of incorrect outputs systematically, and integrating it into live business workflows. If staff are still searching across twenty documents, customer requests are still being classified manually, or managers are still compiling operational summaries by hand, it is a clear sign that an AI project has not moved beyond proof of concept. icitech manages that transition through considered engineering decisions, measurable evaluation, and deep enterprise integration.
02 Context
Not every business problem calls for an LLM. icitech begins by establishing precisely what is needed: knowledge retrieval, summarisation, content generation, structured data extraction, or tool-assisted workflow? That classification forms the basis of the architecture decision. Once source documents, access permissions, currency of information, and sensitive data scope have been assessed, the approach — prompt-only, RAG, structured tool integration, or a hybrid — is selected according to what the business actually requires. Model and vendor selection follows from that step, not before it.
03 Scope
icitech's generative AI services focus on four principal areas.
Language models trained on organisational terminology and authorised sources deliver responses grounded in verified content rather than generic outputs. The retrieval layer is configured according to the permission model, so users receive answers drawn only from content they are authorised to access.
Custom interfaces and back-end systems can be built for use cases such as employee support, customer self-service, product discovery, and operational assistance. The approach applied in JO.ai — converting photo, voice, or text input into structured hotel tasks — illustrates concretely how a generative AI interface can be connected to an operational workflow. The voice and text-based AI approach used in Herapy is an example of positioning AI UX boundaries clearly at the design stage.
Extraction approaches that convert e-mail, form, or free-text content into a defined JSON structure or specific fields connect ticket triage and data-entry workloads to workflow automation. When support request classification is handled this way, staff focus on exception management rather than category assignment.
Generative AI can compile information from authorised data sources into contextual summaries and present decision-ready content to those who need it. For processes with financial, compliance, or security implications, human approval is retained as a control layer.
04 Context
For generative AI, 'the answer looks reasonable' is not an adequate quality measure. icitech builds an evaluation question set during the prototype phase that represents the production use case. Metrics such as answer accuracy, source support, retrieval relevance, fallback accuracy, task completion rate, latency, and cost are all measured against that set. Quality evaluation is not a one-off step; it is maintained after deployment through change regression testing. By the end of a pilot, it becomes measurable whether knowledge search time has decreased, whether the burden of support request classification has changed, or how report preparation time has been affected.
05 Scope
Enterprise generative AI applications require use-case-specific assessment of risks including prompt injection, unauthorised data access, sensitive outputs, hallucination, and tool misuse. For every project, icitech defines prohibited action boundaries, output policy, fallback behaviour, and human approval mechanisms at the design stage. Provider data retention and log confidentiality conditions are established before the project begins. Cost limits and phased user rollout keep the transition to production controlled. Organisations subject to UK GDPR or equivalent data protection regimes will want to confirm that these governance controls align with their own compliance obligations.
06 Context
icitech's generative AI service extends well beyond prompt engineering. Data engineering, API integration, mobile and web layers, and cloud infrastructure are all required together for production AI. The engagement runs from use-case qualification and evaluation through knowledge and access curation, a constrained-scope prototype, quality-latency-cost assessment, API, UI, and integration engineering, guardrail and human-approval design, controlled user launch, and ongoing usage, quality, and cost monitoring. Delivery scope covers: generative AI opportunity assessment; LLM application architecture; retrieval and knowledge ingestion layer; AI assistant interface; enterprise tool integration; evaluation dataset; quality assessment framework; guardrail rules; monitoring dashboard; and technical documentation. Scope can be extended in a structured way as new domains, tools, or use cases are added.
07 FAQ
Requirements such as scalability, latency, and data security that are easy to overlook in a proof of concept become critical failure points under real production load. Architecture decisions therefore need to be taken from the outset with production conditions in mind.
The appropriate architecture depends on the use case, the existing data infrastructure, and security requirements, so there is no single universal answer. Selecting an architecture before completing a use-case analysis typically leads to costly rebuilds.
RAG is generally the more flexible and sustainable option when scenarios require access to current, organisation-specific knowledge. Fine-tuning is the right choice when you need to permanently shape how a model behaves for a specific task or tone.
It is difficult to speak meaningfully about production quality without a systematic evaluation framework covering dimensions such as accuracy, relevance, and consistency. Hybrid approaches combining automated metrics with human review produce the most reliable results.
Employee assistants typically operate against an internal knowledge base that is sensitive to internal documents, processes, and access restrictions. Customer-facing assistants carry additional requirements around brand voice, omnichannel integration, and stricter guardrails.
Prompt injection, unauthorised data disclosure, and the model responding to out-of-scope requests are among the most prominent risks. Managing them requires both technical guardrails and organisational governance policies to be designed together.
Integration complexity depends largely on the maturity of your existing APIs, your authentication infrastructure, and your data transport requirements. A technical discovery exercise early in the engagement prevents unforeseen delays downstream.
LLM-based classification delivers tangible value in situations where rule-based systems fall short, particularly where language variation or contextual interpretation is required. Cost, latency, and explainability trade-offs need to be assessed individually for each case.
Structures that constrain the model to information present in the source document, combined with citation mechanisms, significantly reduce the risk of hallucination. Hybrid workflows in which outputs pass through human review provide an additional assurance layer for critical decisions.
A typical scope includes a working application, architecture and evaluation documentation, and handover materials for the operations team. Knowledge transfer and monitoring infrastructure are included in the delivery scope to support long-term sustainability.