Data
- Responsibility
- Source quality, lifecycle, permissions and provenance.
- Failure signal
- Stale, incomplete or unauthorized context.
- Production control
- Source contracts, deletion paths and ownership.
Services / Production systems
Production AI and LLM systems built to carry operational responsibility.
RSI Tech designs and builds the complete service around the model: data, retrieval, evaluation, integration, release control, monitoring, recovery and ownership transfer.
The operating problem
A model response is only one step in a production decision.
The service must obtain authorized data, measure output quality, handle dependency failure and leave a trace an operator can act on. Those responsibilities remain when a model provider changes.
RSI Tech can work with an existing team or take direct ownership of a bounded delivery. The architecture does not assume one model provider, and provider portability is weighed against the real cost of lowest-common-denominator interfaces.
The complete production path
Retrieval
- Responsibility
- Find and rank the evidence required for the task.
- Failure signal
- Relevant evidence is absent, buried or inaccessible.
- Production control
- Hybrid retrieval, reranking and retrieval evaluation.
Model
- Responsibility
- Transform evidence into the required bounded output.
- Failure signal
- Unsupported, inconsistent or format-breaking output.
- Production control
- Task constraints, model selection and fallback behavior.
Evaluation
- Responsibility
- Measure quality before and after every material change.
- Failure signal
- A release changes behavior without a regression signal.
- Production control
- Versioned datasets, thresholds and release gates.
Integration
- Responsibility
- Connect the workflow to real users, systems and permissions.
- Failure signal
- A useful prototype fails under production state and access rules.
- Production control
- Stable interfaces, idempotency and explicit error states.
Operations
- Responsibility
- Keep the service observable, recoverable and economically bounded.
- Failure signal
- Latency, cost or provider failure becomes invisible operational debt.
- Production control
- Telemetry, budgets, alerts, recovery and named ownership.
Delivery path
From system truth to operable ownership.
- Stage 01
Discovery and architecture
Start with the operating decision, users, system boundaries and failure cost. RSI Tech defines success criteria before scale, then chooses the smallest architecture that can meet them.
- Stage 02
Data and retrieval
Make source ownership, permissions, freshness, deletion and provenance explicit. Retrieval is evaluated as its own system rather than hidden inside model output quality.
- Stage 03
Evaluation
Build versioned examples, thresholds and review paths around the decisions the service must make. A release cannot rely on a promising demonstration or an average score alone.
- Stage 04
Implementation and integration
Connect the service to real product state, identity, permissions and downstream systems. Interfaces expose retries, idempotency, partial failure and human escalation instead of swallowing them.
- Stage 05
Deployment and monitoring
Release through controlled environments with latency and cost budgets, useful telemetry, fallback behavior and a tested recovery path for provider or dependency failure.
- Stage 06
Documentation and handover
Transfer source, tests, operating procedures, decision records and named responsibilities. The receiving team should know how to change, pause and recover the service.
Component decision
Use an LLM where uncertainty is useful and controlled.
Where an LLM fits
Language interpretation, evidence synthesis, extraction from variable documents and bounded generation can justify a model when evaluation covers the real task and users can inspect or recover from uncertain output.
Where it does not
Deterministic lookup, authorization, accounting rules and exact state transitions belong in conventional software. A model should not replace a database constraint or become the only record of a business decision.
Engineering evidence
Relevant mechanisms, with their boundaries visible.
- Evidence 01
Structured extraction, grounding, evaluation and human-review workflow for a high-stakes LLM system.
Read the engineering evidence - Evidence 02
Repeatable data preparation, model comparison and an evaluation workflow designed to reduce regression risk.
Read the engineering evidence - Evidence 03
Explainable signals, controllable thresholds and operational methodology for enterprise telemetry.
Read the engineering evidence
Engagement boundary
Decide fit before expanding the build.
Good fit
A team has a consequential workflow, access to representative data and an owner who can make product and operational decisions. It wants measurable behavior and a system it can operate after delivery.
Wrong fit
The request depends on a guaranteed model outcome, has no lawful path to required data, or treats a polished demo as a substitute for evaluation, integration and operational ownership.
Decision notes
Questions technical buyers ask
- Can you join an internal engineering team?
- Yes. Responsibilities are agreed at the start so architecture, implementation, review, release and operating ownership do not fall between teams.
- Do we need to choose a model first?
- No. The task, data boundary, quality threshold, latency and cost budget should constrain model selection. A provider choice made before those facts are known can lock in the wrong trade-off.
- What does production-ready mean here?
- Measurable quality, controlled access, predictable failure, monitored latency and cost, tested recovery, safe release and a named team able to operate and change the service.