// Private LLM Deployment detailed page in progress
Your models. Your infrastructure. Your data never leaves.
Open-weight LLMs deployed on your cloud or on-prem — with the serving stack, fine-tuning pipeline, and ops tooling to run them like any other production system.
// Who this is for
Built for teams who are past the experiment phase.
BFSI, healthcare, and public sector clients with strict data residency or sovereignty constraints.
Cost-sensitive teams moving high-volume workloads off per-token commercial APIs.
Enterprises standardizing on open-weight models across multiple AI products.
// What we deliver
The scope, in plain language.
Every engagement is scoped against your business outcome, not a fixed menu. What you see below is the typical shape — we tighten it with you in the first week.
- Model selection from the open-weight landscape (Llama, Mistral, Qwen, DeepSeek, etc.) against your quality and latency bar.
- GPU sizing, serving stack (vLLM, TGI, SGLang), and autoscaling on your cloud or on-prem.
- Fine-tuning and domain adaptation pipelines (LoRA, full fine-tune) where justified.
- Evaluation harness comparing private models against commercial baselines on your tasks.
- Ops tooling: observability, rolling upgrades, capacity planning, cost reporting.
// How we work
The Ankor 7-stage framework, applied to private llm deployment.
- 01Discover
Align on business outcome, constraints, and success metric.
- 02Define
Pin down scope, architecture, and the evaluation bar.
- 03Design
Model, data, and UX design — with trade-offs on the table.
- 04Data
Audit, remediate, and pipe the data the build actually needs.
- 05Develop
Ship the system in small, testable increments against the eval bar.
- 06Deploy
Rollout with shadow mode, guardrails, and rollback.
- 07Drive
Operate, measure, and iterate — handoff or retainer.
// Outcomes you can expect
Ranges, not guarantees. Specific, not boastful.
This service page is being expanded. Reach out for a scoping conversation.
Full residency and sovereignty — audit-ready.
Predictable economics at scale.
// Why Ankor
A decade of shipping software, repointed at production AI.
- 10
- years shipping software
- 190+
- clients delivered
- 260+
- products shipped
- 800K+
- daily users served
Serving clients across APAC, the US, and EMEA.
Detailed page coming soon
This service is active — we are writing the full page for a future release. In the meantime, the scope, personas, and outcomes above are accurate. Contact us for a conversation.
// FAQ
Questions we get a lot.
Is this service page complete?
No — detailed content is being expanded. The scope and outcomes above are accurate.
Is a private LLM actually competitive with GPT-4 or Claude?
For many tasks, yes — especially with domain fine-tuning. For others, not yet. We benchmark honestly against commercial baselines on your actual tasks and tell you which category you are in.
Can you deploy on-prem with no internet access?
Yes. Fully air-gapped deployments are in scope, including the fine-tuning and evaluation pipeline.
Do you handle the GPU procurement and sizing?
We size and specify. Procurement usually goes through your cloud or hardware vendor; we advise on contracts and reserved-capacity strategy.
// Ready to ship?
Let's talk about what to build first.
Short call. No deck. We will tell you honestly whether we are the right team for your problem.
// Related services
Keep exploring.
RAG Implementation
RAG pipelines your legal team signs off on.
Grounded, cited, permission-aware retrieval — with evaluation harnesses that catch regressions before users do.
AI Agent Development
Agents that actually do the work.
Multi-step agents with real guardrails, evaluation harnesses, and production observability — not demoware.