Discover
Understand who needs the solution and why.
AI Integration & MLOps · Menteso
Connect AI capabilities to your systems—and keep them reliable.
Integrate approved AI models and services into existing applications, data infrastructure and operations, with deployment, observability and lifecycle controls.
Business-first scopingQuality evaluationHuman oversight
Service overviewFrom a business requirement
From a business requirement
to a defined deliverable.
Integrate approved AI models and services into existing applications, data infrastructure and operations, with deployment, observability and lifecycle controls.
When it is right: Best when an AI capability exists or has been chosen and the challenge is secure integration, dependable deployment and operational support.
How this service differs: This service is about the technical integration and operating layer; the new user-facing product belongs to AI Application Development.
Core capabilitiesWhat the work
What the work
can include.
Every project should have a tailored statement of work, boundaries and acceptance tests. These are typical capabilities rather than a fixed package.
01 / CAPABILITY
System and API integration
Connect AI services to existing software, authentication, CRM, ERP and approved data sources.
02 / CAPABILITY
Deployment and infrastructure
Design staging, production releases, infrastructure environments, capacity and rollback approaches.
03 / CAPABILITY
Monitoring and model operations
Track model versions, availability, cost, drift, application errors and quality signals.
04 / CAPABILITY
Security and lifecycle governance
Enforce access control, secret handling, logging, incident response and ownership of upgrades.
Business applications
Illustrative use cases.
These are examples of potential solutions, not claims about previously completed Menteso engagements.
- Add an AI service to a CRM without rebuilding the entire platform.
- Operate a language-model endpoint for an internal application.
- Deploy model scoring alongside a batch-processing pipeline.
- Monitor several AI services through a common observability layer.
Typical deliverables
What your team receives.
Agreed scope should specify the business outputs, technical artifacts, review criteria and handoff responsibilities.
- Integration architecture and API contracts
- Deployment and configuration plan
- Testing, access and rollback controls
- Monitoring and incident-response runbook
- Operations and handover documentation
Evaluation & deliveryUseful means tested,
Useful means tested,
integrated and maintainable.
Quality gates should be specific to the task, not borrowed from generic AI claims. We recommend agreeing on metrics and a review plan before development.
Measures to agree
- API availability and error rate
- End-to-end latency and throughput
- Model usage costs and drift indicators
- Security findings and recovery readiness
Technology and architecture
Implementation may use API gateways, cloud services, containerized inference, CI/CD, logging platforms and model tracking systems depending on the existing environment.
Selection should reflect system fit, data protection, licensing, cost and operational responsibilities. No provider or model is assumed to be right for every project.
Explore our full development process →Delivery journey01 / STAGE 02 / STAGE 03 / STAGE 04 / STAGE 05 / STAGE 06 / STAGE
From scoping to handoff.
Assess
Review data, permissions, feasibility and risks.
Design
Select architecture, integrations and evaluation criteria.
Build
Implement and test the agreed solution.
Validate
Examine real inputs, edge cases, quality and cost.
Operate
Roll out, monitor, document and improve.
Frequently asked questions
Questions before you begin.
Can we integrate a model built by another vendor?
Often yes, if there are usable APIs, deployment artifacts, license rights and adequate documentation.
Do we need MLOps for a simple chatbot?
A small service still needs monitoring and release controls, though the operating setup should match its complexity and risk.
Will this require replacing our applications?
Not necessarily. We assess current interfaces and favor proportionate integration where technically feasible.
How long does a project take?
Timeline depends on access, data readiness, integration complexity and operational requirements. The proposed milestones follow a scoped feasibility discussion.
How do we start safely?
Share a non-confidential outline of your workflow, users, data sources and desired outcome. Confidential information should use approved sharing methods after appropriate terms are in place.
Related AI services
Connect the right capabilities.
Generative AI Development
Develop generative AI features for text, reports, images, code assistance and structured business content, with workflows that keep quality
Explore service →Custom LLM Development
Evaluate and implement targeted model customization—from task prompting and adapters to fine-tuning and private serving—when generic model b
Explore service →Machine Learning Development
Design, validate and deploy supervised or unsupervised machine learning systems for classification, ranking, anomaly detection, segmentation
Explore service →
