LLM integration, retrieval, agents and multimodal systems — built with the guardrails, observability and evals that keep them working after the launch day.
Production with
Delivery assurance
Organised by architectural layer, not by sales slogan. Every tier has a defined set of deliverables.
Model selection, prompt engineering, context-window strategy, and eval harnesses. Model-agnostic — we pick based on cost, latency, licensing and benchmark fit for your task.
Typical deliverables
Turn your documents, databases and ticket history into grounded, citable answers. Hybrid dense + sparse retrieval, re-ranking, and provenance on every response.
Typical deliverables
Agentic systems that plan, call tools, handle errors and hand off to humans. Budget-bounded, observable, and rollback-safe — built for customer-facing throughput, not leaderboard demos.
Typical deliverables
OCR, invoice & contract parsing, product-image understanding, call transcription, video summarisation. One pipeline across text, image, audio and video.
Typical deliverables
Private copilots that understand your codebase, docs and runbooks. IDE plugins, PR reviewers, and bespoke chat surfaces — trained and retrieved against your SSOT, not the public web.
Typical deliverables
Churn, demand, fraud, recommendation, propensity. Trained on your data, evaluated against your business metric, retrained on a schedule you can audit.
Typical deliverables
A shape you can expect in the statement of work — adapted to your stack and threat model.
Docs, DBs, SaaS & events
Chunk · embed · index
Hybrid search + rerank
Prompt · tools · agents
Pick per task
Safety · policy · budget
API · UI · copilot
Horizontally scroll on mobile · reference architecture
We stay model- and cloud-agnostic. Selection is driven by cost, latency, licence fit and eval performance for the task.
LLMs
Orchestration
Vector & search
Cloud
Evals & ops
A structured path that de-risks AI adoption at every stage.
Workflow audit, data review, and a one-page scope with a measurable eval target. Engineering sign-off before any code ships.
End-to-end prototype on real data. Proves out the riskiest assumption first so the business case is decidable in a fortnight.
Guardrails, rate limits, caching, fallbacks, observability, security review. Everything between a demo and an SLA.
Weekly eval review, prompt / retrieval / model iteration, drift and cost monitoring. Change tracked against the eval score.
Two-week, fixed-scope discovery. You leave with a scoped architecture, an eval target, and a costed build plan — whether or not you engage Cord4 for the build.
Fixed scope · Full code ownership · Reply within 24 hours