Comptech AI Platform
Your private, enterprise AI platform, run in your own data centre and supported in Bangladesh.
Overview
Enterprise AI, inside your own data centre
Comptech AI Platform brings generative AI, agents, RAG and the full machine learning lifecycle onto your own GPUs, with prompts, documents and models kept inside your network.
It comes with guardrails, single sign-on, a secrets vault and DC-DR resilience already integrated, and is supported by Comptech engineers in Bangladesh.
Banking accelerators
Ready for Bangladesh banks
Accelerators built for banking, included as platform features.
Bank data agent
Managers ask questions in plain English and get answers from live data, with read-only access.
Real-time fraud scoring
ML models score each transaction as it happens.
Bangladesh-ready guardrails
Detect and mask NID numbers, Bangladeshi mobile numbers and account numbers.
Features
Generative AI and model serving
High-throughput LLM serving
Run leading LLMs on your own GPUs, with fast responses and a lower cost per token.
Distributed inference at scale
Split large models and heavy traffic across GPUs and servers, with request routing that keeps response times predictable.
Models-as-a-Service
Offer approved models to every team through a governed internal API, instead of paying per token to outside providers.
Classic ML serving
Deploy credit, fraud and churn models next to your LLMs, with autoscaling.
Capabilities
Model catalog and registry
Curated model catalog
Start from tested, optimized models that are ready to deploy.
Model registry
Version, approve and promote every model, with a full audit trail.
Capabilities
Agents, tools and RAG
Agent runtime
Build AI agents that reason, call tools and act, all through one consistent API.
Secure tool connectors (MCP)
Connect agents to core banking, CRM and document systems through governed, permissioned connectors.
Enterprise knowledge search (RAG)
Ingest PDFs, circulars and policies, and get grounded answers with citations.
AI Studio and playground
Try prompts, documents and agents in the browser before going to production.
Capabilities
AI safety and governance
Guardrails
Block toxic, off-topic or unsafe content, and mask personal data in prompts and answers.
Model evaluation
Benchmark models on accuracy and safety before release.
Bias and drift monitoring
Track fairness and data drift on models in production.
Tracing and observability
Trace each agent step, tool call and model response.
Capabilities
Build and train
Workbenches
Jupyter, VS Code and RStudio in the browser, with GPU access on request.
ML pipelines
Automated, scheduled and versioned workflows from data to deployment.
Fine-tuning
Adapt models to your data with efficient LoRA/QLoRA tuning across GPUs.
Distributed training
Scale jobs across nodes with built-in queueing.
Feature store
Reuse consistent features across training and real-time scoring.
Capabilities
GPU-as-a-Service
GPU sharing and quotas
Pool GPUs, set fair-share quotas for each team, and queue jobs so expensive hardware never sits idle.
Multi-vendor accelerators
NVIDIA, AMD and Intel accelerators.
Capabilities
Enterprise foundations
Air-gapped deployment
Install and run with no internet connection.
Single sign-on
One login across every tool, with AD/LDAP and MFA.
Secrets vault
Keys and credentials kept and rotated centrally.
DC-DR resilience
Tested failover between your data centres.
Multi-tenancy and RBAC
Isolated projects per team, with role-based access.
Architecture
Share GPUs, serve models, keep control
Models, agents and notebooks run as workloads on a shared GPU pool. Queues and quotas decide who gets capacity, guardrails sit in front of every model, and RAG and tools connect to your data with read-only access.
- Training jobsDistributed runs, queued fairly between teams.
- Inference servicesLLMs and ML models served with autoscaling.
- Notebooks and experimentsWorkbenches that fit on a slice of a GPU.
- GPU utilisationRises as teams share the capacity you own.
Capabilities
Capability map
Generative AI and model serving
- Capabilities
- High-throughput LLM serving, Distributed inference at scale, Models-as-a-Service, Classic ML serving
Model catalog and registry
- Capabilities
- Curated model catalog, Model registry
Agents, tools and RAG
- Capabilities
- Agent runtime, Secure tool connectors (MCP), Enterprise knowledge search (RAG), AI Studio and playground
AI safety and governance
- Capabilities
- Guardrails, Model evaluation, Bias and drift monitoring, Tracing and observability
Build and train
- Capabilities
- Workbenches, ML pipelines, Fine-tuning, Distributed training, Feature store
GPU-as-a-Service
- Capabilities
- GPU sharing and quotas, Multi-vendor accelerators
Enterprise foundations
- Capabilities
- Air-gapped deployment, Single sign-on, Secrets vault, DC-DR resilience, Multi-tenancy and RBAC
Banking accelerators
- Capabilities
- Bank data agent, Real-time fraud scoring, Bangladesh-ready guardrails
Use cases
Banking use cases
- Customer chatbot (web, app, WhatsApp)
- Agent copilot for the call centre
- Policy and circular assistant with citations
- KYC and loan document processing
- Fraud and AML alert explanation with STR drafting
- Credit memo drafting
- Complaint triage against Bangladesh Bank deadlines
- HR/IT helpdesk
Included
With every Comptech product
On premises or private cloud
Runs in your own data centre, so data stays in the country. Open formats and standard APIs avoid lock-in.
Single sign-on
Keycloak with AD/LDAP, MFA and group-based roles across every tool.
Secrets vault
Credentials and keys held centrally in an OpenBao vault, not in configuration files.
DC/DR design
Designed for a primary data centre and a disaster recovery site.
Local support and SLAs
Comptech engineers in Bangladesh, with Bronze, Silver or Gold SLAs.
Pilot and training
A pilot in 4 to 6 weeks, plus training for your team.
How to engage
01
Discover
Agree the first use case, the systems involved and the success criteria.
02
Pilot
Deploy on your infrastructure and prove the use case in 4 to 6 weeks.
03
Production
Add the DR site, single sign-on and the remaining teams and workloads.
04
Support
Local support under a Bronze, Silver or Gold SLA, with training for your team.
Questions
How long does a pilot take?
A typical pilot runs 4 to 6 weeks on your hardware or private cloud, with one agreed use case and success criteria set at the start.
What support do you offer?
Local support from Comptech engineers in Bangladesh, with Bronze, Silver and Gold SLA levels. We also train your team to run the platform day to day.
Are we locked in?
No. The platforms use open formats and standard APIs, so your workloads and data stay portable.
Request a pilot
Tell us about your first AI use case. We'll plan a 4 to 6 week pilot on your own GPUs.
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