LLM Integration Services: RAG, AI APIs & Agents — Shipped With an Eval Report

It usually starts with a roadmap promise: "AI assistant — shipping Q2." A freelancer builds a demo that answers beautifully on the five questions asked in the sales call, then invents a policy in front of a customer in week three. There's no eval suite, no documentation, and the vendor stops replying two weeks after the final invoice. This isn't bad luck — Upwork demand for AI integration work grew 178% year over year in 2026, so supply rebranded overnight and quality variance among vendors is now extreme.

MIT NANDA's research found that 95% of enterprise gen-AI pilots produce no P&L return — unmeasured quality is how integrations quietly join that statistic. The real risk in LLM integration isn't whether a demo can be built; anyone can build one. It's whether anyone can prove the system still works on input #500 — which is an evals problem, not a model problem.

The outcome is always the same shape: a feature or internal system live in your existing product, passing its agreed eval threshold, documented for your team to maintain. Three things typically get built: RAG on your documents (knowledge assistants, support deflection, document Q&A, with retrieval tuning, chunking, and groundedness evals); LLM API features inside your product (summarization, extraction, classification, copilots, shipped through your normal PR workflow); and agent workflows that touch your CRM, ERP, or ticketing system, with guardrails and human-in-the-loop policies where they matter.

Every block ends with the same question answered in writing: what does the eval measure, and did the build pass it.

We integrate OpenAI, Claude, and Gemini API scopes behind an abstraction layer, so the model is a configuration decision revisited at every eval run — not an architecture commitment. This service fits SaaS founders and CTOs with a scoped feature, GCC IT teams with a document-heavy internal use case, and agencies reselling delivery under their own brand. It is not the right fit if you still need to work out which AI use case to pursue at all (start with the AI Readiness Sprint instead) or if you're planning a $500K+ multi-system program (that's AI Implementation).

Fixed scope works because Stage 1 removes ambiguity — "working" is defined in test cases, not adjectives. The ops retainer exists because model providers deprecate APIs on their own schedule, not yours. Assess: a scoping call becomes an integration spec — systems touched, data sources, a golden set (typically 50 test cases, Arabic and English where relevant), the eval threshold, and named exclusions — delivered as a fixed-price SOW in 2–5 business days. Pilot: we build in staging inside your repo for 1–4 weeks, with evals run continuously and weekly build notes.

Prove: limited production traffic or UAT, a final eval run against the contractual threshold, handover docs, and a recorded walkthrough — then a 30-day fix window starts. Scale and Operate follow as needed: change-order phases for more data sources, more channels, or an Arabic rollout, and an ongoing ops retainer from $1,000/month covering monitoring, re-run evals on every model or prompt change, and model migrations — with a runbook that's yours to keep.

The floor price is shown up front; the exact quote follows the scoping call. Exclusions are stated on every offer: fine-tuning, more than 500 documents (priced per tranche), and SSO or enterprise auth are change orders, printed at the same rates in the SOW before you sign. Every build can attach an ops retainer from $1,000/month — monitoring, evals on every model change, migrations. Without it you own a well-documented snapshot; with it, a maintained system.

Payment terms: SKUs are 100% upfront; larger fixed scopes are 50% deposit and 50% on acceptance. Pricing is quoted in USD on both locales, with SAR/AED equivalents available in proposals for GCC buyers.

Before we have published client case studies, we show you the artifacts instead: a sample eval report — an ungated PDF of a real eval run on our own internal RAG build, with a methodology page, golden-set excerpt, per-language scores in Arabic and English, and failure analysis. This is what you receive from us, and what you should demand from any vendor.

We never fabricate logos or testimonials. Where we don't yet have client proof, we show you the methodology instead — including, once it ships, our own live RAG assistant on this site, built with the same $2,400 SKU pipeline, with its own published eval scores.

Model APIs: OpenAI, Anthropic Claude, Google Gemini, and open-weight models, including Arabic-capable models where evals justify them. Vector stores: pgvector, Qdrant, and Pinecone. Orchestration: LangChain, LlamaIndex, and provider SDKs, chosen per scope with no framework religion — plus n8n where a workflow engine beats custom code.

We connect to your systems — HubSpot, Salesforce, Zoho, Zendesk, Intercom, ERPs, internal APIs, and Postgres, MySQL, SharePoint, or Notion as document sources — and deploy into your own cloud project (AWS, Azure, or GCP), with regional hosting options for PDPL and GDPR scopes. Everything ships in your repositories and your accounts — no proprietary lock-in, ever.