Generative AI
Custom LLM fine-tuning, retrieval-augmented generation over your private domain knowledge, and internal AI assistants — built with evaluation pipelines, guardrails, and observability from day one. Auditable, not magical.
What We Build
Secure retrieval over your documents, wikis, tickets, and databases — with permission-aware access, source citations, and answers your compliance team can stand behind.
When prompting hits its ceiling, we fine-tune open or hosted models on your domain language and tasks — with versioned datasets and regression evals so quality only moves one direction.
Copilots for support teams, analysts, and engineers that answer from approved knowledge and act through approved tools. Productivity without shadow-IT risk.
Golden datasets, automated eval suites, red-team prompts, and content guardrails — the unglamorous machinery that separates a demo from a deployment.
Why Algogile
We define quality metrics and test sets before building, so 'it feels better' is never the acceptance criterion.
Tenant isolation, permission-aware retrieval, and data residency handled at the design stage — not patched in after the security review.
We architect so you can switch or mix model providers as pricing and capability shift. Your moat is your data and your system, not a vendor contract.
Questions, Answered
No. Your data stays within your infrastructure or your private cloud tenancy, and our architectures use providers' no-training API tiers or self-hosted models, per your compliance requirements.
Usually RAG first: it's cheaper, auditable, and updates instantly with your content. Fine-tuning earns its cost when you need consistent domain tone, structured outputs, or latency on smaller models. We benchmark both before recommending.
With evaluation suites: curated test questions, scored answers, regression tracking across releases, and human review sampling. You see the score move, not just a demo.
One use case, your real documents, a measurable evaluation score. That's the POC.