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Custom GenAI Solutions

Purpose-built AI agents, copilots, and workflow automation designed around your actual operations.

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What You Gain

  • A production-deployed AI agent, copilot, or automated workflow that handles a defined, measurable portion of a current manual process.
  • Documented time savings or error reduction, benchmarked against the baseline process before deployment.
  • A system your team can operate without the original developers — complete with runbook, monitoring, and escalation paths.
  • Safety controls and audit logging that satisfy your compliance and governance requirements.
  • A maintenance-ready architecture with clear procedures for prompt updates, model upgrades, and integration changes.

What We Deliver

  • Solution Architecture Document (model selection rationale, integration design, data flow, safety controls)
  • Deployed production system (agent / copilot / automation — fully integrated with your specified systems)
  • Prompt library with version history and management instructions
  • Integration documentation for all connected systems
  • Operational Runbook (monitoring, alerting, escalation, maintenance procedures)
  • AI Governance documentation (use case register entry, risk classification, oversight controls)
  • Staff training session and reference guide

This Service Is Right for You If…

You have a specific, repetitive cognitive task that a human currently does by reading documents, querying systems, and producing structured output — and you want to automate part or all of it.
You have piloted a commercial AI tool and found it does not integrate with your source systems, or produces output that requires too much human correction to be worth using.
Your data residency or compliance requirements prevent you from using US-hosted AI APIs, and you need an on-premises or Canadian-cloud deployment.
You have a security operations team that is drowning in alert volume and needs AI-assisted triage without replacing analyst judgement on complex cases.
You want to deploy AI in a regulated function (financial services, healthcare, legal, insurance) and need governance controls, audit logging, and explainability built in from the start.

Frequently Asked Questions

How is a custom AI solution different from just using ChatGPT or Microsoft Copilot?

Commercial tools are general-purpose. A custom solution is designed around your specific data, systems, and workflows. It integrates directly with your CRM, ERP, policy documents, or security tools. It operates within safety boundaries appropriate for your industry. The trade-off is build cost and maintenance responsibility — which is why custom solutions make sense for high-frequency, high-value workflows.

How long does it take to build a custom AI agent or copilot?

A focused copilot connected to two or three data sources, with a defined scope and clear success criteria, takes four to eight weeks from kick-off to production deployment. A multi-step agent with several tool integrations takes eight to fourteen weeks. The biggest driver of timeline is data readiness.

What happens when the AI model we are using gets updated or deprecated?

Mitiksha builds systems with model abstraction layers that make swapping the underlying model straightforward. The maintenance retainer explicitly covers model migration. We test against the new model version before switching, validate output quality on benchmark queries, and update prompts to account for behaviour differences.

We are in a regulated industry. Can you build AI systems that meet our compliance requirements?

Yes. Mitiksha builds compliance controls into GenAI systems from the start: structured audit logging of all inputs and outputs, human oversight checkpoints for high-stakes decisions, output filtering for sensitive information, role-based access controls, and governance documentation aligned with NIST AI RMF. Our AI Governance & Risk practice runs in parallel with build engagements for regulated clients.

Can you build AI systems that work on our own servers, without sending data to the cloud?

Yes. Mitiksha deploys AI systems on client infrastructure or Canadian-hosted cloud environments using open-source models (Llama 3, Mistral, Phi-4) that can be run locally. Performance on domain-specific tasks is close to frontier models for many use cases. We benchmark the local model option against the API option during the discovery phase.

How do we know the AI system will keep working accurately six months after deployment?

It requires active maintenance. Prompts that work today may drift as your source data changes, the model is updated, or your workflow evolves. Mitiksha's operational runbook includes a monthly quality review, alerting for output anomaly rates, and a defined process for prompt updates. AI systems are not set-and-forget infrastructure.

Last reviewed April 2026

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