AI that reachesproduction.

AI engineering is a core discipline at Ceylon Labs AI, not an add-on. A dedicated AI team works alongside our software engineers on every engagement, so the model, the data and the application are owned by one accountable team.

Discuss an AI project How we evaluate AI

The disciplines inside our AI practice

RAG & LLMs

Answers grounded in your own knowledge, with citations and measured groundedness, not confident guesses.

Select a node to explore. The field turns and parts as your pointer moves through it.

AI services.

From a first retrieval prototype to multi-agent systems embedded in enterprise platforms, each measured against thresholds you agree before we build.

Generative AI
Content, code and decision support built on foundation models, with guardrails and evaluation.
Custom LLM Applications
Domain-specific assistants and copilots tuned to your data, tone and workflows.
RAG Systems
Retrieval pipelines over your documents and systems with measured groundedness and citations.
AI Agents
Tool-using agents that complete multi-step work inside your existing software.
Multi-Agent Systems
Coordinated agents with clear roles, shared memory and supervision.
Machine Learning
Prediction, classification, forecasting and computer vision models, trained and monitored.
Enterprise AI Solutions
AI embedded into core business platforms with security, access control and auditability.
Intelligent Automation
End-to-end automation of document-heavy and decision-heavy processes.
AI Integration
Bringing models into your ERP, CRM, data platforms and customer channels.
AI Architecture & Consulting
Strategy, architecture and model selection grounded in what reaches production.

Agents you can audit.

Every step an AI system takes is logged, measured and reviewable. Illustrative samples from an agent run and a retrieval experiment.

Agent trace · run 8c41

Grounded

Support agent · 5 steps · 2.1 s · $0.004

1PlanFind the refund policy for annual plans, then check this customer’s plan.
2Tool · search_policies3 passages returned · top match “Refunds, §4.2”
3Tool · get_accountPlan: annual · renewed 12 days ago
4GuardrailNo personal data in the reply · tone check passed
5AnswerEligible for a full refund within 14 days of renewal [§4.2].

Every step an agent takes is logged and reviewable, with citations to its sources.

Experiment log

r-23 promoted

Retrieval for long technical manuals · recall@5

Run	Change	Recall@5
r-14	Baseline retrieval	0.71
r-17	+ hybrid search	0.78
r-21	+ re-ranker	0.84
r-23	+ query rewriting	0.87

Each idea is tested against the same evaluation set; only measured gains reach client work.

Evaluated before it ships.

Every AI system passes an evaluation stage: accuracy, robustness and failure modes against thresholds agreed in writing. Nothing goes to production on a good demo alone.

Agreed up front
Success criteria and evaluation thresholds are signed before the build begins.
Measured honestly
Groundedness, accuracy, refusal behaviour, latency and cost, reported in full with failures included.
Watched in production
Drift, quality and cost are monitored after launch, with retraining planned, not improvised.

Planned certification: ISO/IEC 42001 for AI management systems. Our assurance roadmap

What you hold at the end of a stage.

Not slides. Working documents with thresholds agreed before the build, results measured against them, and a decision you sign. Shown here as illustrative samples.

AI evaluation report

Approved

Support assistant (RAG) · release candidate 3

Metric	Agreed	Result
Groundedness	≥ 0.90	0.94passed
Answer accuracy	≥ 0.85	0.88passed
Out-of-scope refusal	≥ 0.95	0.97passed
Hallucination rate	≤ 2.0%	1.2%passed
p95 latency	≤ 2.5 s	1.9 spassed

All five thresholds met. Approved for production by the client’s product owner.

ADR-007

Accepted

Architecture decision record

Vector store for retrieval

Context: 2.4M documents, strict tenant isolation, data must stay in-region.
Decision: PostgreSQL with pgvector in the client’s own cloud account.
Consequence: One database to operate; re-index window planned monthly.

Production monitoring

Live

Model drift · last 30 days

Uptime: 99.97%
Cost / 1k req.: $0.42
Alerts: 0 open

Drift has stayed inside tolerance for 30 days; no retraining needed this cycle.

Bring us the process, the documents or the product. We will tell you honestly what AI can do for it.