AIR API — AI Retrieval API

Overview

AIR API is a shared AI retrieval platform built at Spotware to answer a simple but high-impact question: how do you let every AI tool in the company reach the same trusted knowledge without each team reinventing its own retrieval stack?

The platform serves grounded answers from corporate knowledge sources — the public Help Centre and the cTrader Admin Guide — through two interchangeable interfaces: a REST API for general AI systems and a standards-compliant MCP (Model Context Protocol) server for MCP-aware AI clients. Content is refreshed twice daily, so answers stay current without manual effort.

The Problem

Large corpora of documentation create three failure modes for AI-assisted work:

• Keyword search misses intent. Traditional search returns pages, not answers, and struggles with paraphrase, translation, and multi-part questions.
• Models can't ingest everything. AI systems cannot load a full knowledge corpus into context. Without a grounding layer, they hallucinate.
• Knowledge fragments per tool. Different teams use different AI tools (general-purpose assistants, agentic coding IDEs, bespoke apps). Without a shared retrieval layer, each re-indexes the same content and the company ends up with several competing, drifting copies of its own knowledge.

AIR API was built to solve all three at once — as a single retrieval backbone the rest of the company can plug into.

The Solution

AIR API exposes a compact, extensible retrieval surface:

• Two interchangeable interfaces: REST API + MCP server. The REST API integrates with general-purpose AI systems and custom applications; the MCP server integrates with MCP-aware AI clients. Both interfaces serve the same underlying capabilities, so teams can adopt whichever matches their stack.
• Multiple knowledge sources out of the gate. The initial rollout covers the public Help Centre and the cTrader Admin Guide, with the platform designed so additional corporate sources can be onboarded without rebuilding the core.
• Automatic twice-daily refresh. Content is ingested on a schedule so retrieval stays current as documentation evolves.
• Advanced retrieval quality. The platform handles multilingual queries by translating them into the document's language, decomposes complex questions into simpler sub-questions, and expands queries with multiple rewrites to improve recall. A two-layer storage design keeps small semantic units for precise matching while returning the larger parent documents users actually need — and de-duplicates results so a document is never returned twice for the same query.

Integrations

AIR API was designed from day one as a shared backbone, and in its first year it became the common retrieval layer behind several distinct workflows inside Spotware:

• AI-assisted development and analysis. Engineers, analysts, and product managers reach AIR API from MCP-aware tools such as Claude Code and Windsurf — the same retrieval surface powers coding, documentation, specification, and discovery workflows.
• Corporate ChatGPT, including as a Company Knowledge source. AIR API's MCP server meets the standards-compliant search and fetch method requirements needed to register as a Company Knowledge source in corporate ChatGPT, so teams get grounded, source-linked answers directly in the assistant they already use.
• Grounded ChatGPT assistant. Dedicated cTrader Support assistant is grounded on AIR API's retrieval, giving users direct, source-backed answers instead of raw documentation pages.
• Trader-support automation. AIR API serves retrieval for automated responses in the trader-support pipeline — a capability Spotware referenced publicly in its 2025 highlights announcement: "an AI-driven automation solution integrated with our internal knowledge base analyses incoming enquiries and generates responses automatically. As a result, 60% of trader enquiries are resolved by AI in an average of three minutes."

Measurable Impact — 2025 Year-End Results

Scoped to calendar year 2025, based on production observations:

• 8,000+ retrieval requests served.
• ~5 seconds average response time per request.
• Zero errors across the observed production volume.
• Under $8 total retrieval cost across that volume.

The low cost reflects a deliberate design choice: rather than pushing every query at a premium model, AIR API uses a layered retrieval pipeline that resolves most requests with inexpensive operations and reserves heavier AI calls for the parts of the query that actually need them.

Business Outcomes

• A single retrieval backbone that teams can plug into instead of each building their own. New AI use cases get grounded answers on day one, not after a multi-week retrieval project.
• Immediate unlock for downstream automation — including grounded assistants, agentic developer tooling, and automated trader support in Spotware's 2025 operations.
• Scalability by design. Additional corporate knowledge sources can be onboarded without rebuilding the core platform, so the retrieval layer grows with the company rather than being recreated.

My Role

As AI Product Manager at Spotware, I led AIR API from concept to production — framing it as a shared retrieval platform rather than a single-app tool, defining the quality bar and impact goals, picking the interfaces (REST + MCP) that would maximize downstream adoption, aligning engineering and the consuming teams (Support, analysts, developers, PMs), and driving integration into concrete workflows across the company. For the broader role context, see AI Product Manager at Spotware.