Governed knowledge
Sources, owners, access policy, and update cycles are known.
RAG · AI ASSISTANTS
RAG and AI assistant development
We build assistants that work with your documents, knowledge bases, and business systems. The scope goes beyond a chat UI: access control, ingestion, updates, retrieval, cited answers, evaluation, observability, and integrations all matter.
01 · DIRECT ANSWER
Where to start
RAG fits when answers must rely on a governed corpus and expose evidence. Quality depends on access controls, data freshness, retrieval and answer evaluation, abstention when evidence is insufficient, and human escalation—not only the model.
02 · FIT
When this approach fits
Evidence-linked answer
The user needs the document and passage supporting a conclusion.
03 · INPUTS AND OUTPUTS
Stage deliverables
Inputs for discovery
- Corpus and owners
- Roles and access controls
- Evaluation questions and expected sources
- Abstention and escalation policy
- 01
Discovery
Workflow, data, risk, baseline, and explicit pilot decision criteria.
- 02
Pilot
One end-to-end flow, a versioned evaluation set, error log, and comparable measurement.
- 03
Production
Roles, access, monitoring, versions, incident procedures, and a validated operating boundary.
04 · SYSTEM
Architecture and integrations
Retrieval
Ingestion, chunks, index, access filters, retrieval, and reranking are evaluated separately.
Answering
Context, citations, faithfulness, abstention, and instruction injection controls form one system.
05 · DECISION
Continue or stop gates
| Decision | Condition |
|---|---|
| Continue | Value and quality are supported by comparable evidence, while residual risk and TCO are acceptable to the workflow owner. |
| Narrow the scope | Value exists, but some actions, sources, or error classes require a smaller AI role. |
| Stop | Data is unavailable, the result cannot be observed, deterministic automation is better, or residual error cost is unacceptable. |
How the result is measured
- Quality
- A versioned set of real scenarios, error types and severity, abstention, and manual corrections.
- System behaviour
- Latency, availability, cost per workflow, integration failures, and drift after changes.
- Business workflow
- Comparison with the baseline: cycle time, manual touches, throughput, or another preselected measure.
Operations and total cost
- Observability
- Logs that minimise sensitive data, decision traces, alerts, and incident review.
- Change control
- Data, model, prompt, and integration versions pass the evaluation set and have a rollback path.
- TCO
- Discovery, integrations, model calls, infrastructure, monitoring, human review, and support all count.
Risks and limitations
- Data and rights
- Data scope, processing basis, provenance, storage, and access are established before a pilot.
- Rare high-cost errors
- Average quality does not hide critical classes; those use constraints, abstention, or human decision.
- Dependencies
- External models and APIs can change price, limits, and behaviour; material dependencies need fallback or replacement.
RELATED CASE STUDIES
Verifiable project stage
PRIMARY SOURCES
Method basis
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksarXiv
- RAGAS: Automated Evaluation of Retrieval Augmented GenerationarXiv
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileNIST
- OWASP Top 10 for Large Language Model ApplicationsOWASP Foundation
These sources support the method and do not validate AI SaaS Solution project outcomes.
07 · FAQ
RAG and AI assistant FAQ
Does RAG eliminate hallucinations?
No. RAG can improve grounding, but errors remain possible. Test sets, citations, narrow scope, and human review are needed for critical decisions.
Can document permissions be preserved?
Yes. Access rules must be enforced during retrieval so users receive context only from sources they are authorised to view.
Can it use PDFs, a Wiki, CRM, and databases?
Often yes, but each source needs separate validation. Structure, extraction quality, freshness, permissions, and update mechanisms affect the result.
Can the system run in a private environment?
It depends on the infrastructure and model choice. We evaluate APIs and open-source options against data, cost, and quality requirements.
START A PROJECT
Test an assistant on your data
Share sample sources and 10–20 real questions. That creates a practical basis for technical diagnosis and evaluation.