Production AI systems

Take AI from prototype to a system operations can run.

GillTech AI designs and builds AI-powered applications with retrieval, tool use, evaluation, guardrails, and observability — integrated into the software the business already depends on.

Book a Consultation Discuss Your Project

Problem

Many AI efforts stop at a demo. Models are wired into a chat box, costs are unclear, answers cannot be trusted, and there is no path into CRM, ERP, or internal APIs. The result is a prototype that cannot survive production traffic, audit, or change.

Solution

GillTech AI treats AI as application infrastructure. The company designs the application layer, the AI layer, retrieval and tools, and the connections into business systems — then instruments the whole path so quality, latency, and cost can be managed.

Capabilities

LLM application design
RAG and knowledge assistants
AI agents and tool / function calling
AI APIs and workflow orchestration
Evaluation and guardrails
Observability and cost control
Production reliability
Integration with existing applications

Use cases

Knowledge assistants grounded in company documents and systems of record

Decision-support products that sit inside existing operational software

Document intelligence that extracts, validates, and writes structured data

Customer and operations copilots with defined actions and escalation

Production AI, in depth

The building blocks GillTech AI uses to take AI from a prototype into software operations can run.

LLM applications

Intelligent software features powered by large language models, scoped to a real business task rather than an open-ended chat box.

RAG

Retrieval over proprietary documents and systems of record so answers can be grounded, cited, and refused when evidence is missing.

AI agents

Task-oriented workers that retrieve knowledge, call tools, follow rules, and stop when a person must decide.

Tool calling

Constrained function and API access so models can read and write inside the systems operations already run.

AI APIs

Stable application interfaces for AI capabilities that other products and workflows can call.

Workflow orchestration

Multi-step AI and business-rule flows with state, retries, and human approval gates.

AI evaluation

Task-based scoring so quality is measured against real work, not conversational fluency.

Guardrails

Allow-lists, policy checks, and stop conditions for actions that are hard to undo.

Observability

Traces, logs, latency, and cost so production AI can be operated like any other service.

Cost optimization

Caching, routing, and model choice against measured quality — not unbounded token spend.

Enterprise integration

Connections into CRM, ERP, APIs, and data stores so AI can act on operational truth.

Production deployment

Rate limits, fallbacks, rollbacks, and operational ownership after go-live.

Architecture

Representative production AI architecture (example, not a client-specific design).

Delivery approach

Start from the business task and the systems of record. Define what the AI layer is allowed to do, how it retrieves knowledge, which tools it may call, and how success is measured. Build incrementally with evaluation in the loop, then harden for production: logging, tracing, rate limits, fallbacks, and human review where risk is high.

Technology

LLM APIs RAG Agent / RAG / Tools Tool / function calling PostgreSQL Redis REST APIs Evaluation harnesses Application observability

Outcome

AI that is part of the operating platform: observable, maintainable, and useful against real workflows rather than a disconnected chat experiment.

Ready to build, modernize, or automate?

Tell us what the business is trying to build, improve, or automate. Our team can help identify the right technical approach.