AI Strategy & Generative AI Development
Private / Self-Hosted AI Model Deployment
Deployment of open-weight language models on your own servers or private cloud, so sensitive data never leaves your control while teams still benefit from generative AI.
Overview
Why it matters
for your business.
Banks, hospitals, government bodies and firms holding client confidential data often cannot send information to public AI APIs. Data residency rules, contracts or internal policy simply forbid it. We deploy open-weight models such as Llama, Mistral or Qwen inside your infrastructure, on-premises GPU servers or a private cloud region in India, behind your firewall and identity controls.
We size hardware to your workload, choose a model that balances quality with cost, and apply quantisation where it saves resources without hurting results unacceptably. The serving stack is hardened, monitored and patched like any other production system. We also benchmark the private model against your real tasks, because open models may trail hosted ones on some jobs and you deserve to know where.
What's included
- Model selection and hardware sizing report
- Secure model serving stack on your infrastructure
- Private API endpoint with authentication and rate limits
- Benchmark results on your representative tasks
- Monitoring for GPU usage, latency and errors
- Runbooks for updates, scaling and model upgrades
Outcomes
What changes
for your business.
Ideal for
- Banks, NBFCs and insurers
- Healthcare and legal firms with confidential records
- Government and public-sector institutions
Data stays in-house
Prompts, documents and responses remain within your network, supporting DPDP Act obligations and client confidentiality commitments.
Stable costs at scale
Heavy, steady workloads run on owned or reserved capacity rather than variable per-token charges.
Full model control
You decide when to upgrade, fine-tune or retire a model, independent of any provider’s roadmap.
How we deliver
A clear, accountable
process.
Every engagement follows the same disciplined approach, adapted to the size and urgency of your project.
- 01
Identify
We find the use cases and processes where AI or automation clearly pays off, and estimate the impact.
- 02
Prototype
A proof of concept on your own data shows what works before you commit to a full build.
- 03
Deploy
We integrate with your systems, add guardrails and human review, and train your team.
- 04
Monitor & improve
Accuracy, cost and usage are tracked, and the solution is tuned as your data and needs change.
Ways to work with us
- Fixed-price projectsDefined scope, milestones and deliverables.
- Implementation & integrationOne-time technology deployment and integration.
- Time & materialFlexible delivery based on actual effort and resources.
- Monthly managed servicesRecurring application, IT, marketing or infrastructure support.
FAQ
Common
questions.
Something else on your mind? Ask us directly.
Are self-hosted models as capable as hosted models like GPT or Claude?
For many focused tasks such as summarisation, extraction and internal Q&A, recent open-weight models perform well. For complex reasoning they can lag the largest hosted models. We benchmark both on your actual tasks so the trade-off between privacy, quality and cost is clear before you invest in hardware.
Do we need to buy GPU servers?
Not necessarily. Options include renting GPU instances in a private cloud region, using a dedicated hosted environment, or buying servers if usage is high and steady. Smaller quantised models can even run on modest hardware for light workloads. We compare these options with you.
Let's talk about Private / Self-Hosted AI Model Deployment.
Tell us what you need and get a clear plan and proposal, free and with no obligation.