LLM Integration Consulting

Put large language models
to work in your company.

We deploy private LLMs, fine-tune models on your data, and redesign workflows that AI can actually improve. No fluff, no vendor lock-in — just engineers who've built production AI systems.

What we do

End-to-end LLM integration,
from strategy to production.

We cover the full stack — infrastructure, models, data pipelines, and the human processes that make AI useful.

RAG & Knowledge Systems

Connect your model to internal documents, databases, and knowledge bases. Get accurate, cited answers grounded in your actual company data.

  • Document ingestion pipelines
  • Semantic search (pgvector, Qdrant, Elasticsearch)
  • Hybrid retrieval & re-ranking
  • Hallucination mitigation

Model Fine-tuning

Adapt a base model to your domain, tone, and tasks. LoRA and full fine-tuning on your proprietary datasets for precision your use case demands.

  • Domain adaptation (legal, finance, support)
  • Instruction tuning & RLHF
  • Evaluation & benchmark design
  • Quantization for deployment

Process Automation

Identify the workflows bleeding the most time, then redesign them with LLM agents. Document processing, email triage, report generation, internal Q&A.

  • AI agent design & orchestration
  • Integration with existing tools (Slack, CRM, ERP)
  • Human-in-the-loop workflows
  • ROI measurement framework

AI Strategy & Audit

Before writing a line of code, we map your organisation's AI readiness, identify the highest-ROI opportunities, and build a prioritised roadmap.

  • Use-case discovery workshops
  • Data quality & readiness assessment
  • Build vs. buy analysis
  • Executive-ready roadmap deck

Ongoing Support & Ops

LLMs in production need monitoring, retraining, and iteration. We provide embedded engineering support so your AI capabilities compound over time, not degrade.

  • Model drift monitoring
  • Prompt versioning & A/B testing
  • On-call engineering retainer
  • Monthly performance reviews
Our process

From first call to
production deployment.

We move fast. Most engagements go from discovery to a working prototype in under four weeks.

01

Discovery call

A 45-minute session to understand your tech stack, team, and the specific workflows you want to improve. Free, no commitment.

30–45 min
02

Technical assessment

We audit your data, infrastructure, and current processes. We identify two or three high-impact LLM opportunities and estimate effort and ROI for each.

3–5 days
03

Proof of concept

We build a working prototype scoped to the highest-value use case. You see real results on your own data before any larger commitment.

2–4 weeks
04

Production rollout

Full implementation, integration with your existing systems, team training, and documentation. We don't leave until it's running smoothly.

4–12 weeks
Why aore.ai

Engineers who ship,
not decks that slide.

Most AI consulting looks like this: a strategy presentation, a vendor recommendation, and a retainer that outlasts the enthusiasm. That's not us.

We're a small team of engineers who have deployed LLMs in production at fintech, iGaming, and enterprise SaaS companies. We write the code, set up the infrastructure, and stay until it works.

Talk to an engineer
48h
Median time to first prototype
100%
Private deployments — your data stays yours
No lock-in
Open-source stack by default
Java / Kotlin Python Kubernetes vLLM LangChain pgvector Elasticsearch Kafka Spring Boot Temporal Redis PostgreSQL
Delivered projects

Production AI systems
we've shipped.

Not demos. These are live systems serving real users, running on real data.

Get started

Let's figure out what LLMs
can actually do for you.

Book a free 45-minute discovery call. We'll look at your stack, understand your workflows, and tell you honestly whether LLMs will move the needle — and how.

No commitment, no pitch deck
You'll talk to an engineer, not a salesperson
Response within 24 hours

We typically respond within one business day.