Easy to demo, hard to ship
LLM selection, RAG knowledge bases and agents, deployed privately
The difficulty is not the model; it is data supply, evaluation, cost and safety boundaries. We start with a feasibility assessment, then model selection and benchmarking, prompt engineering and RAG knowledge bases, fine-tuning and quantisation, and multi-agent orchestration with tool calling, delivering applications that are auditable, operable and costed, on private and domestic-stack infrastructure.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Feasibility and cost are assessed before a project is approved
The assessment stage benchmarks candidate models, analyses data supply and calculates ROI / TCO. Use cases without the conditions to succeed stop here; the assessment report is yours.
The evaluation set is built before development
Each use case gets an evaluation set and baseline score before development starts. Every change to prompts, retrieval or model is scored the same way, so quality changes are traceable and launch decisions rest on data.
Private and domestic-stack deployment
Models and applications deploy inside your network and adapt to domestic GPUs and operating systems. Distilled and quantised models run on CPU or a few GPUs; the compute plan is part of the assessment.
Deliverables
Assessment and selection report
Candidate model comparison, ROI / TCO and a compute plan, to decide first whether it is worth doing.
A running application
Knowledge base, agents and APIs with an evaluation set and baseline score; every iteration is scored the same way.
Operations manual
Monitoring, drift detection, guardrails and a cost dashboard, so your team can take over after launch.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Use case assessment
Identify candidate use cases, benchmark models, analyse data supply, cost the options, recommend whether to proceed.
Proof of concept
Build the evaluation set and a minimum viable version; verify quality and cost on real data.
Pilot launch
Integrate with business systems and permissions, deploy guardrails and monitoring, launch to the first user group.
Iterate and operate
Iterate against evaluation metrics, extend to further use cases, or hand operations to your team.
Customer story
Reallysec Builds a Modern AI-Driven Search Platform for Cisco
Enterprise search on Cisco.com rebuilt with Elastic: 73% faster, with 90% of requests handled automatically.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
Selection is based on benchmarking for the use case rather than vendor rankings. During assessment, candidate models are compared on your real data for quality, latency and unit cost, alongside the deployment environment (public cloud, private or domestic stack) and data compliance requirements. Open, closed and domestic models are all candidates; the solution is not tied to one vendor.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.