The Bibha platform
Connect the model to the rest of the work.
Prepare data, develop and evaluate models, build agents and workflows, and run them through APIs or real-time voice. Bibha connects the assets, infrastructure, controls and operating evidence around the complete business system.
How does the Bibha platform work?
Bibha connects data, models, agents and workflows in a shared platform. Teams prepare and version data, develop and evaluate models, connect knowledge and tools, and deploy applications through APIs or voice. Compute, governance and observability support operation, while shared workspaces preserve the relationships between assets, releases and results. Expert teams can co-build or manage the system.
Keep the whole system together.
Organise related data, models, prompts, agents and releases around a business use case. Shared workspaces and projects give teams a place to find reusable assets, assign owners and follow the relationships from development work to operating results.
Keep development, testing and production configurations separate. Before retiring an asset, review where it is used and what depends on it.
- Your data
- Prepare data
- Model catalogue
- Train
- Evals + QA
- Ready to deploy
Prepare the data. Develop the model. Evaluate the result.
Model development connects three kinds of evidence: the examples you use, the configuration you run and the results you accept. Bibha provides the tools to keep those decisions traceable.
Data preparation
Import and organise datasets, prepare examples and training segments, review quality and duplicates, and keep dataset versions. Separate training data from test cases, record provenance and permitted use, and prepare sensitive fields and expert annotations.
Model training and fine-tuning
Compare model candidates, reuse training recipes and manage runs with progress, logs, cancellation and supported recovery. Keep checkpoints and model versions, compare experiments and hand a selected version to deployment. Review model size and eligible artefact exports around the task and licence.
Evaluation and release quality
Maintain business test suites, compare with a baseline and evaluate complete agents and workflows. Add human review, regression and boundary tests, compare quality, speed and cost, and retain versioned results and the release acceptance record.
Give agents context, actions and boundaries.
Build an agent around a defined job using prompts, a selected model, knowledge and tools. Add memory for permitted continuity, guardrails for outputs and actions, and structured results for the business process.
Version prompts, compare their effects and test the agent in a controlled workspace. Route uncertain or consequential work to a person, and reuse approved agent configurations across suitable applications.
Connect knowledge and business systems with the right access.
Manage reference sources, retrieve relevant material and show the source supporting an answer. Keep knowledge fresh and limit retrieval to the information a user or task may access.
Connect operational systems through tools, with credentials managed separately from instructions. Distinguish information lookup from actions that change records, test connections before use and surface connection failures when they need attention.
Make workflows complete the process.
Run backend work from business events or schedules. Set conditional steps and approvals, preserve task state and resume work through a multi-step process.
Use retries, timeouts and duplicate-action protection around dependencies. Collect exceptions for review, verify completion in the connected system and set execution limits for duration, steps and spending.
Carry the same business task into live conversation.
Build voice interactions with speech-to-text, text-to-speech and real-time model exchange. Connect telephony, configure languages and voices, handle turn-taking and interruptions, and transfer suitable interactions to a person with context.
Apply the approved disclosure and consent process to interaction records. Capture transcripts, recordings and structured outcomes, and measure the complete response path. Local or private voice configurations are available for the qualified speech and application stack.
Serve models and manage the environment underneath.
Provide or connect compute and GPU resources, run training and processing jobs, and serve selected models to applications. Manage capacity, concurrency, scaling and resource allocation around the workload.
Operate deployment releases with health monitoring, recovery, rollout and rollback, plus backup and restore for agreed assets. Set resource budgets and quotas, and move a workload to a compatible model or provider when required.
Set authority and data boundaries explicitly.
Control platform access and the roles allowed to view, edit, execute and release assets. Apply policy rules and isolation boundaries, retain audit history, and define who can approve production changes.
Manage data location, retention and deletion requirements alongside supplier and dependency records. Document encryption and key responsibilities, maintain evidence for security commitments, and provide emergency stopping and access revocation. Export and transition assistance are defined around eligible assets and agreed rights.
See what happened, what it cost and what to improve.
Follow task traces and activity history. Track failures, latency, throughput, resource health and usage, alongside the business criteria the work needs to meet. Alerts connect agreed operating limits to responsible people.
Capture feedback, prioritise an improvement backlog and evaluate changes before release. Analyse model choice, context use, caching and routing for the configured workload, with quality and cost assessed together.
An illustrative request, with the controls around it.
Suppose a service team wants an AI system to answer a customer question and prepare an operational follow-up. This example shows how the platform parts connect; it is not a customer case study.
Receive the request
A person uses a voice or customer-facing entry point, or an existing application initiates work through an API.
Retrieve the relevant context
The agent uses permission-aware knowledge and a tool connection to bring in the relevant business information.
Act through the workflow
The workflow prepares the response and next action. An approval step pauses sensitive work for a person; completion checks verify the resulting change in the connected system.
Review and improve
Task history, outcomes and usage inform the review. Test a proposed model, prompt or workflow change against the business cases before approving a new release.
Choose the delivery team as well as the technology.
Co-build with Bibha on solution design, data preparation, models, applications and deployment, with operator enablement and handover. Or choose managed operation with outcome, cost and improvement reviews.
For software and delivery partners, Bibha also provides partner and embedded-product enablement. Define the application, responsibilities and commercial rights for the proposed arrangement.
Evaluate the configuration your workflow will use.
Start with the task, the systems it must connect to and the conditions it must meet. Use the parts of Bibha you need alongside existing models and tools.
Confirm the models, data types, providers, hardware and integration combinations for that design. Set the deployment boundaries, workload expectations, acceptance criteria and operating responsibilities before putting the system into use.
Explore in detail
One connected platform. Explore every capability.
Workspaces and AI assets
Organise AI projects, discover shared assets, trace dependencies and manage ownership, environments and retirement on the Bibha platform.
Data preparation and datasets
Prepare AI datasets, training segments and review examples. Validate quality, preserve versions and manage data rights, sensitive fields and retention.
Model training and fine-tuning
Choose, train and fine-tune models for business tasks. Manage recipes, runs, checkpoints and versions, then evaluate and deploy the selected model.
AI evaluation and release quality
Evaluate models and complete AI systems against business tests. Compare quality, speed and cost, review boundaries and preserve release evidence.
Agent configuration and behaviour
Build AI agents with instructions, knowledge, tools, workflows, memory and guardrails. Test behaviour, manage changes and route work for human review.
Workflow execution and automation
Build visual AI workflows with triggers, schedules, conditions and approvals. Manage state, retries, duplicate protection, exceptions and verified completion.
Knowledge and integrations
Connect AI to approved knowledge and business systems. Manage sources, retrieval, access, credentials and actions around the work your team needs done.
Realtime voice and interaction
Build spoken AI interactions with speech processing, telephony, turn-taking and human transfer. Define the voices, records and deployment your workflow needs.
Model serving and compute
Put models into operation with inference, compute, job execution and deployment management. Scope capacity, release controls and recovery around your workload.
Governance and customer control
Define access, data boundaries and change authority for AI systems. Review roles, audit history, retention, dependencies and the evidence behind agreed controls.
Monitoring and improvement
Review AI task traces, reliability, speed, usage and cost. Connect operating evidence and feedback to accountable decisions about what to improve.
Questions and answers
No. You can use an external model with connected knowledge and tools, or train and adapt a model when the task calls for it. Model comparison and task-specific evaluation help assess the approach before selecting a version for use.
An agent uses prompts, knowledge, memory and tools to handle a defined job. A workflow organises the surrounding process, including triggers, conditional steps, approvals, retries and completion checks. They can be used together in the same business system.
No. Knowledge retrieval supplies information while the system handles a request. Training and fine-tuning change the model itself. Bibha supports both, with separate data, knowledge and evaluation capabilities so you can choose the approach the work needs.
Use representative business tests to evaluate the model and the complete agent or workflow. Compare against a baseline, include human review and boundary tests, and retain versioned evidence with the acceptance decision. Define the thresholds around the workload.
Yes. Bibha supports modular use and business-system connections. Confirm the particular model, provider, data and integration combinations for the proposed configuration. You do not need to adopt every platform capability at once.
No. Feedback and operating evidence may lead to changes in knowledge, prompts, workflows, models or infrastructure. Bibha supports an improvement backlog, evaluation and controlled releases. Retraining is one possible change when the task and evidence justify it.