RAG & Knowledge Systems
A fluent answer is not enough if the system retrieved the wrong evidence.
Hit@K · Recall@K · MRR · nDCG · groundedness · citation accuracy · latency · cost
Explore this service →Our approach — Reliability by Design
Reliability is engineered, not assumed.
An AI system can look impressive in a controlled demo and still fail when it meets real documents, real users, edge cases, changing data, and production constraints.
At Relytic, the system and the evaluation process are developed together so technical decisions can be tested against representative business scenarios rather than intuition alone.
Whole-system quality
A reliable AI system is more than an accurate model
Failures can come from data, retrieval, OCR, prompts, integrations, permissions, latency, model updates, or the interaction between components. That is why we evaluate the complete workflow.
- Retrieve the correct source
- Refuse when evidence is insufficient
- Preserve difficult table structures
- Use the correct tool and arguments
- Control false positives and negatives
- Preserve performance after changes
- Meet product latency and cost needs
Our approach
Seven decisions from workflow to dependable system
01
Understand the real workflow
We start with the business process rather than the AI architecture: who uses the system, what it supports, where data comes from, what happens when it is wrong, and where human review is required.
This prevents a project from optimizing a technical benchmark that does not reflect the real problem.
02
Define what success means
Before optimizing, we establish representative evaluation data, expected outputs, acceptance criteria, failure categories, and the metrics that matter for the workflow.
The objective is to make trade-offs explicit before they become production problems.
03
Build the system and the evaluation together
Evaluation is part of development, not a final audit. We test components independently and as part of the end-to-end workflow.
Evidence guides decisions about retrieval, reranking, OCR, model choice, agent autonomy, and deployment optimization.
BuildMeasureFind failuresImproveMeasure againFIG. 05 — BUILD THE SYSTEM AND THE EVALUATION TOGETHER 04
Investigate failures, not just averages
Aggregate metrics can hide the cases users care about most. We slice performance by meaningful conditions and inspect why the system fails.
A useful evaluation should tell us what to improve next—not simply produce a score.
05
Add controls where the workflow needs them
Depending on consequence and risk, reliability may require citations, abstention, validation rules, approvals, permissions, audit logs, or escalation.
The amount of control should match the business risk.
06
Treat production behavior as part of quality
Latency, throughput, cost, external services, infrastructure limits, security, maintainability, monitoring, and incoming-data changes are part of the real system.
We consider these constraints during development rather than after the prototype.
07
Monitor and regression-test important behavior
Models, prompts, documents, data distributions, and integrations change. Evaluation sets and regression tests keep important capabilities measurable after those changes.
Production signals feed back into the next engineering cycle.
Different systems, different evidence
Reliability looks different for every AI system
AI Agents & Workflow Automation
The more actions an agent can take, the more important controlled execution becomes.
Task completion · tool correctness · recovery · approval boundaries · auditability · execution time
Explore this service →Document AI
A parser can have good average OCR accuracy while still failing on the tables that matter most.
CER · WER · field accuracy · table structure · reading order · processing failures
Explore this service →Computer Vision
The deployment decision should reflect the complete accuracy–speed–cost trade-off.
Precision · recall · F1 · mAP · failure slices · latency · throughput · memory
Explore this service →Existing AI products
Already built an AI system?
We can create an evaluation framework around an existing RAG system, agent, LLM application, extraction pipeline, or vision model to determine where it performs well, where it fails, which failures matter, and whether proposed changes actually improve it.
Evidence over intuition
- Hundreds of thousands
- of enterprise documents
- 84.5% → 92.0%
- mAP@50
A multilingual knowledge environment across 7 languages and thousands of daily queries.
Industrial vision improved through failure analysis, data changes, benchmarking, and optimization.
The principle is simple
Evidence over fashion.
We define the behavior the system needs, build representative tests, compare alternatives, investigate failures, and use the evidence to decide what to change.
That is what Relytic means by reliability by design.
Next step
Building an AI system where reliability matters?
Book a 30-minute conversation with Relytic to discuss the workflow, the risks, what needs to be measured, and what a practical engineering approach could look like.