Generative AI Integration Services for US & EU Software Teams

Provider selection is an eval problem, not a marketing problem. We build a task-specific eval set of 150 to 400 representative inputs with graded golden outputs, then score candidate models on quality (LLM-as-judge plus human spot-check on a 50-item subset), latency at the p50/p95/p99 we actually need, and cost per 1k requests at our token mix. Typical lineup: GPT-4o or Claude 3.7 Sonnet for reasoning, GPT-4o-mini or Claude 3.5 Haiku for classification, gpt-4o-realtime for voice, an open-weights model on vLLM for cost-sensitive batch. We re-run the eval every quarter because the frontier moves.

RAG is right when answers live in a corpus that updates faster than you can fine-tune (policies, product docs, tickets, contracts). It is the wrong answer when the model already knows the domain (general code, general knowledge), when latency is below 500ms p95, or when you need deterministic outputs that fine-tuning produces more reliably. We frequently combine: RAG for grounding in customer data, a small fine-tuned model for output structure or domain vocabulary, and prompt engineering for orchestration. Pure RAG is rare in production. Pure fine-tuning is rarer.

Three layers. At the ingress, a PII detector (Microsoft Presidio or a fine-tuned classifier) strips or tokenises emails, names, phone numbers, and account IDs before the prompt leaves our infrastructure. At the provider, we contract zero data retention with OpenAI, Anthropic, and Bedrock (signed BAAs where applicable) and prefer EU-hosted endpoints for EU data. At egress, output is scanned for hallucinated PII before being shown to the user. DPAs are signed before kickoff, and we maintain a sub-processor list aligned with your customer-facing contracts.

Prompts are code. They live in version control, they are reviewed in pull requests, and they are tagged with semantic versions that get logged on every inference. Each prompt change runs against a regression eval set in CI (Ragas for RAG quality, DeepEval or a custom rubric for task-specific metrics), and we will not merge if any tier-1 metric regresses by more than 2 percent. In production we log prompt version, model, latency, and token cost per request through LangSmith, Helicone, or Langfuse so you can A/B prompts the same way you A/B features.

Yes. Most engagements integrate into an existing backend (Node, Python, Go, Java) and existing infra (AWS, GCP, Azure) rather than running on a separate platform. We do not push you to a vendor-specific orchestrator if you do not need one. We pair with your engineers, run code reviews together, and write ADRs for the architectural calls so the choices outlive our engagement. Knowledge transfer is contractual: by end of the pilot, your team owns the codebase, the evals, and the runbook.

Engagements are fixed-scope and lean, all-in and quoted in USD. A PoC runs from $2,900 (4–6 weeks); RAG over your knowledge base from $8,100; an autonomous AI agent from $10,400; an ML model in production from $13,800. The exact number depends on how many use cases you ship, corpus depth and eval-set size, latency targets and compliance scope. You see the line-item budget at the end of discovery and sign off before any code is written. There is no recruitment markup and no tool surcharges, and cloud and token spend run on your own accounts, so you keep the cost lever.

Three categories consistently waste budget. First, deterministic lookups: if the correct answer is always a database query or a rules engine output, adding an LLM adds latency and hallucination risk without upside. Second, low-volume, high-stakes decisions with no feedback loop: a model that writes one insurance underwriting decision per week and never gets corrected cannot improve, and the error cost is too high to accept. Third, vanity chatbots: a GPT-4 wrapper in front of a FAQ page that nobody reads is still a FAQ page nobody reads. We include a “do not build this” list with reasoning in every discovery output, and we will tell you when a use case belongs on it.

A PoC runs 4–6 weeks: discovery, eval harness, working prototype, go/no-go memo. A RAG system over your knowledge base, from kickoff to a customer-zero deployment behind a feature flag, typically runs 10–14 weeks including corpus ingestion, retrieval tuning, PII handling and observability. An autonomous AI agent adds 2–4 weeks on top of that for orchestration design, human-in-the-loop hooks and runbook. An ML model in production from a notebook baseline is 14–20 weeks depending on data readiness. The largest variable is always your data: clean, labelled, accessible data cuts weeks off every stage.

Yes, and often we mix them. Open-weights models on vLLM (Mistral, Llama, Qwen) make economic sense for high-volume, latency-sensitive batch classification where a hosted frontier model would cost 10x more at the same quality. We evaluate open-source on the same task-specific eval set as GPT-4o and Claude — if the quality gap is acceptable at the throughput and latency you need, we ship the cheaper option. Self-hosting on your own GPU cluster or a managed inference endpoint (AWS SageMaker, GCP Model Garden, Azure AI) is something we can architect and hand over. We do not pretend open-source is always the answer: for complex reasoning tasks the quality gap is real and the eval will show it.

We treat hallucination as an engineering problem with four levers. Grounding: RAG replaces parametric recall with retrieval from a verified corpus, so the model cites a source rather than confabulating one. Evals: a regression harness in CI catches quality regressions before they reach users — we catch the hallucination in testing, not in prod. Output filtering: structured output schemas (Pydantic, instructor) constrain the response format; for high-stakes fields, a secondary classifier or rule engine validates the output before it is shown. Human-in-the-loop: for decisions above a confidence threshold or in high-stakes categories, we route to a human rather than auto-completing. The combination means hallucination rate in production is a measurable SLO, not a “we hope it works” situation.