Artificial Intelligence

The problem

General models cannot answer questions about your policies, contracts, tickets or product data, and guessing is worse than refusing.

What we implement

An ingestion and retrieval pipeline: document parsing, chunking strategy, embeddings, hybrid search, reranking, and generation constrained to retrieved context with citations.

How it works

Retrieval quality is measured separately from generation quality, so failures are attributed correctly. Access control is applied in the query so users only retrieve what they may read.

Expected outcome

Answers are grounded in sources users can open and check, and the system declines rather than inventing when evidence is missing.

The problem

Multi-step processes need tool use and decisions, but unconstrained agents are unpredictable and hard to audit.

What we implement

Supervised agent workflows with a defined tool surface, explicit state, step and cost limits, deterministic handling for anything consequential, and human approval checkpoints.

How it works

Each tool call is validated and logged, so a completed run can be replayed and reviewed. Deterministic code handles what does not need a model.

Expected outcome

Automated workflows that can be inspected step by step, with humans retaining control of decisions that matter.

The problem

Without evaluation, every prompt change is a guess and every model upgrade is a risk.

What we implement

Task-level evaluation datasets drawn from real inputs, automated scoring for what can be scored, structured human review for what cannot, and regression runs in CI.

How it works

Evaluation runs on every meaningful change and on model version updates, with results tracked over time so quality movement is visible rather than anecdotal.

Expected outcome

Changes are shipped on evidence, and a model or prompt update that degrades a case is caught before users see it.

The problem

Critical information sits in scanned PDFs, forms and inconsistent templates that resist search and extraction.

What we implement

A parsing pipeline with layout-aware extraction, OCR where needed, schema-constrained structured output, confidence scoring and review queues for low-confidence results.

How it works

Extraction returns typed data validated against a schema, and anything below a confidence threshold is routed to a person rather than passed downstream silently.

Expected outcome

Structured, checkable data from unstructured documents, with uncertainty surfaced instead of hidden.

The problem

AI features fail differently from ordinary services, and standard monitoring does not show why an answer was poor.

What we implement

Tracing of prompts, retrieved context, tool calls, tokens and latency; caching; model routing; rate limiting; and per-feature cost budgets with alerting.

How it works

Traces link a user complaint to the exact context and calls that produced the response, which makes debugging a data question rather than a guessing exercise.

Expected outcome

Production behavior is observable, spend is bounded and predictable, and rollouts are staged rather than global.

The problem

Deployment stalls because nobody can explain what the system will and will not do with sensitive data.

What we implement

Input and output filtering, PII handling rules, prompt injection defences, data retention decisions, model and vendor selection criteria, and documented human oversight.

How it works

Controls are implemented in code and tested like any other behavior, and each decision — including which data may reach which provider — is written down.

Expected outcome

Security and legal reviewers get concrete answers about data handling and system limits instead of assurances.