AI Model Fine-Tuning, Deployment & Evaluation Systems

What This Solution Covers

A production AI model platform spans four concerns that most teams underestimate as a single problem: training (fine-tuning data, methods, experimentation), inference (serving infrastructure, optimization, cost), evaluation (offline benchmarks, online quality, regression), and operations (monitoring, governance, compliance).

We help engineering teams build all four as a coherent platform — not as four disconnected projects that have to be stitched together later.

When You Need This Stack

Most teams reach for this platform when:

• Frontier API costs are dominating the unit economics at sustained production volume.
• Domain-specific output quality consistently underperforms what prompt engineering or RAG can fix.
• Compliance, residency, or air-gap requirements rule out hosted frontier models.
• Latency requirements make API round-trips a hard constraint.
• The team needs reproducible, evaluable, governable model releases — not vibes-based shipping.

If none of those apply, the platform investment is hard to justify. We help teams answer this honestly.

How the Pieces Fit Together

A coherent stack has six layers:

1. Dataset curation and versioning — instruction tuning data, synthetic generation, validation, regression sets.
2. Fine-tuning pipeline — LoRA / QLoRA / full fine-tuning on Llama, Mistral, Phi, Qwen, or commercial bases.
3. Evaluation harness — automated metrics, behavioral tests, regression tests, human-in-the-loop. Wired into CI.
4. Inference serving — vLLM, TGI, TensorRT, or managed equivalents with quantization and continuous batching.
5. Production operations — token telemetry, latency, hallucination, drift detection, cost telemetry, rollback.
6. Governance — prompt versioning, experiment tracking, audit logs, PII handling, compliance evidence.

The new article series under this solution covers each layer in depth. This page is the overview; deep dives live in the implementation guides below.

How We Engage

For most clients, we run the platform build as a 12–20 week engagement: discovery and architecture (weeks 1–2), foundational platform (weeks 3–8), first production model end-to-end (weeks 9–14), hardening and handoff (weeks 15–20). Smaller engagements deliver specific layers — typically a fine-tuning pipeline + evaluation harness, or an inference platform + observability — in 6–10 weeks.

Every engagement ends with the client team's engineers operating what we built. We invest in runbooks, dashboards, and training because platforms nobody can operate end up replaced.

Why Choose Boolean & Beyond

We have built and operated SLM platforms across financial services, healthcare, manufacturing, and B2B SaaS — including engagements where compliance ruled out hosted frontier APIs and where cost ruled out unbounded API spend. The patterns in this solution come from production deployments, not theoretical exercises.

We are based in Bangalore (sales) and Coimbatore (engineering centre). Engagements are designed to leave the platform with the client team — not to create dependency on us.