AI Integration

AI software development that ships —
not another demo.

Senior-led AI integration for startups and regulated industries. We build RAG pipelines, compliance automation, real-time translation, and AI agents into real products — grounded in your data, monitored in production, and audited where it matters. 12+ years of shipping production code, three production AI deployments and counting.

What we build

Where AI earns its place.

Six patterns we ship repeatedly — each one validated in production, not just in a notebook.

RAG pipelines

Retrieval-Augmented Generation that grounds LLM output in your real product data — docs, catalogs, knowledge bases, code. Not hallucinated answers; cited, verifiable ones, with pgvector / Cloudflare Vectorize indexing tuned for latency.

Compliance automation

AI screening against sanctions / PEP / AML datasets — 1.7M+ records — that turns a 4-hour manual review into 45 seconds of compute. Built for regulated fintech: auditable, deterministic where it matters, LLM-flexible where it helps.

Real-time translation

On-demand LLM translation inside chat and product surfaces, routed across providers (Claude, GPT, Groq) by query complexity. Sub-500ms p95 latency for live UX, not batch jobs that run overnight.

AI agents & workflows

Agents that replace multi-step human workflows — triage, extraction, classification, follow-up. The boring, repeatable 80% automated so your team handles the judgment-call 20%. Production-monitored, not demo-ware.

LLM integration into existing products

Adding intelligence to a codebase you already run — React/Next.js, React Native, Supabase, AWS — without a rewrite. API layer, prompt management, evals, cost controls, and fallbacks wired into your existing stack.

Vector search & semantic retrieval

Beyond keyword search: embeddings that match intent, not just strings. Useful for support deflection, in-app discovery, internal knowledge tools, and as the retrieval backbone under RAG.

How it works

From audit to production in weeks.

01

Audit where AI actually earns its place

Most products don't need AI everywhere — they need it in 1–2 high-leverage spots. We map your workflows, flag the AI-amenable 20%, and explicitly call out where AI would hurt. Free 30-min call, no deck.

02

Ship a thin production slice in weeks

Not a 3-month POC. A real, monitored feature behind a feature flag — RAG over one dataset, one automated workflow, one translation surface — deployed to your actual users with evals and cost ceilings.

03

Harden, expand, hand off (or stay on)

Once the slice earns its place: harden the prompts, build the eval harness, add observability, then either hand off to your team with docs or keep iterating as a retained partner. Senior-only throughout.

Stack

Models and tools we reach for.

Provider-agnostic by design — we route per-query so you're never locked to one vendor.

  • Claude (Anthropic)
  • OpenAI GPT-4o / o-series
  • OpenRouter (multi-model routing)
  • Groq (low-latency inference)
  • Supabase pgvector
  • Cloudflare Workers AI
  • LangChain / LlamaIndex (where they earn it)
  • n8n (workflow orchestration)

Client words

What teams say after shipping.

"He integrated AI-driven AML screening into our compliance pipeline across 1.7 million sanctions records. It actually works in production, not just a demo."
Kenneth Founder, HubSecure
"Riz understood the compliance constraints from day one. In regulated fintech, that kind of reliability is rare in an external partner."
Venu Director, Qwil
"We needed senior-level execution without the ramp-up time. Riz shipped production features faster than most full-time hires I've seen."
Sahil Gupta Founder & CEO, Noah

FAQ

Questions, answered.

What does AI integration cost?

Engagements start at €2,000/month, retained. There's no hourly billing and no surprise invoices — you get consistent senior bandwidth. Most teams ship a thin production slice in the first 4–6 weeks; cost depends on scope, model usage, and how much of your existing stack is in place.

How is this different from wrapping the OpenAI API?

A wrapper calls a model and shows the output. Production AI integration handles the parts that actually matter at scale: retrieval (RAG) so answers are grounded in your data, prompt management and versioning, evals so you can detect regressions, cost controls and model fallbacks, observability, and the security/compliance review regulated industries need.

Can you add AI to an existing codebase without a rewrite?

Yes — that's the most common engagement. We add an AI layer alongside your existing React/Next.js, React Native, Supabase, or AWS stack: an API service, a retrieval index, and a UI surface behind a feature flag. Your team keeps shipping while the AI slice is built and measured in parallel.

Which models do you use?

Whichever fits the job. Claude for reasoning and long-context work, GPT-4o for general tasks, Groq-hosted models for sub-second latency, and open models via Ollama where data residency or cost demands it. We route per-query through OpenRouter so you're never locked to one provider.

Do you work with regulated industries (fintech, health)?

Yes — compliance automation is one of our core areas. The HubSecure engagement screened against 1.7M+ sanctions records under real fintech constraints: deterministic matching where auditors require it, LLM flexibility where it helps, full audit trails. We're not a substitute for your compliance team, but we speak the language.

Get started

Not sure where AI fits your product?

Free 30-minute Technical AI Audit. We'll look at your product together and tell you exactly where AI would (and wouldn't) add value. No pitch deck, no obligation.

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