Production-grade SLM platform
Trusted by 100+ innovative teams
What we build
Enterprise-grade SLM pipelines for domain adaptation, cost-efficient inference, observability, and model governance across training and production.
Built for teams like yours
How we deliver
Map your workflows, identify high-impact opportunities, and quantify ROI potential.
Build a focused MVP for your highest-impact use case in 4-6 weeks.
Harden, monitor, and expand — leveraging existing infrastructure for each new capability.
4-8 weeks
pilot to production
95%+
milestone adherence
99.3%
SLA stability
AI Model Fine-Tuning, Deployment & Evaluation Systems Implementation
Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.
4-8 weeks
pilot to production timeline
95%+
delivery milestone adherence
99.3%
observed SLA stability in ops programs
Deep dives
Technical articles on building production ai model fine-tuning, deployment & evaluation systems systems.
Deep dive
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.
Most teams reach for this platform when:
If none of those apply, the platform investment is hard to justify. We help teams answer this honestly.
A coherent stack has six layers:
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.
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.
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.
Fine-tuning earns its complexity for consistent output format, domain reasoning the base model lacks, smaller models distilled to match larger ones, or reliable refusal/policy enforcement. It is the wrong call when you need fresh factual knowledge (use RAG) or when prompt engineering on a stronger base model would solve the problem. We help teams make this decision honestly — and recommend RAG or prompt engineering about 40% of the time.
Default to LoRA for most production behavioral changes — comparable quality to full fine-tuning at ~1% of the compute and memory. QLoRA when you need to fine-tune very large models (70B+) on a single GPU. Full fine-tuning rarely earns its cost for production tasks; we reach for it only when the marginal quality gain is genuinely necessary and budget allows.
For self-hosted high-volume deployments, vLLM is the default — continuous batching, PagedAttention, highest throughput per GPU dollar. TGI for HF-centric teams. RunPod, Modal, or Together AI for managed inference at lower volume. The right choice depends on traffic shape, ops capacity, and total cost — we benchmark before committing.
A production evaluation harness layers automated metrics (faithfulness, answer relevance, latency, hallucination rate), behavioral tests (refusals, formatting, edge cases), regression tests (previously-failed queries), and human-in-the-loop review for high-stakes domains. We build the harness before the first fine-tune so every change is measured against a stable baseline.
Yes — we deploy across public cloud, customer-tenancy (AWS Bedrock VPC, Azure OpenAI in subscription), and fully on-premise. For air-gapped environments we use open-weight models (Llama, Mistral, Qwen) with no internet egress. Architecture choice depends on data classification, residency requirements, and the team's ops capacity, which we assess in the first week.
A working production-grade system the client team operates after we leave. That includes: fine-tuning pipeline with versioned datasets, evaluation harness wired into CI, inference infrastructure with rollback automation, observability dashboards, runbooks, and model governance artifacts. We do not deliver Jupyter notebooks; we deliver platforms.
Explore related services, insights, case studies, and planning tools for your next implementation step.
Delivery available from Bengaluru and Coimbatore teams, with remote implementation across India.
Case Studies
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002