i-360 services
AI Implementation Services
AI implementation services turn an approved opportunity into a working, integrated application. I-360 acts as an AI implementation partner across architecture, engineering, system integration, deployment, evaluation, and operational handover.
- Business problem
- Opportunity assessment
- Prioritize
- Architect
- Prototype
- Integrate
- Deploy
- Evaluate
- Monitor
- Scale
What implementation actually includes
Enterprise AI implementation is more than connecting a prompt to a model. A usable solution needs an application workflow, authorized data access, a defined output contract, failure handling, evaluation, and a person responsible for operation. The model is one component in that system.
Our starting point is a business problem and an agreed result: an employee can find approved knowledge, an operator can review extracted records, or a team can complete a controlled workflow. We identify what can be tested before selecting the model or delivery approach.
Opportunity assessment and use-case prioritization
An AI opportunity assessment reviews the current process, people, data, systems, pain points, and operating constraints. Candidate use cases are compared by usefulness, feasibility, data readiness, integration effort, risk, and the ability to evaluate the result.
If the opportunity is still uncertain, enterprise AI consulting can establish the roadmap first. AI transformation implementation begins when a selected use case has an owner, accessible information, a review process, and a scope that can be delivered.
Architecture and a contained prototype
Architecture defines the application, model provider, retrieval components, storage, interfaces, permissions, and deployment environment. Generative AI implementation may use RAG, tool calling, structured extraction, or ordinary software rules in combination. Local and cloud choices are compared against task quality, infrastructure, data policy, and operating costs.
A proof of concept answers a specific uncertainty with representative examples. It is not automatically production-ready. The next decision records what the prototype demonstrated, what failed, which dependencies remain, and what engineering is needed for a supported release.
Data and system integration
AI integration services connect the solution to approved sources and existing business systems. We specify data ownership, access boundaries, update frequency, payload validation, authentication, retries, and reconciliation. A model response must pass the application's validation before it becomes a stored record or action.
RAG implementation introduces ingestion, retrieval, evidence, and knowledge governance. AI agent development adds scoped tools, business rules, approvals, and audit events. Workflow automation handles deterministic routing and state transitions around the AI step.
Security, governance and release readiness
Before enterprise AI deployment, we agree authentication, authorization, sensitive-data handling, secret storage, logs, retention, review responsibilities, and escalation. Retrieved content and user input are treated as data, not permission to override system controls.
Release readiness includes deployment configuration, environment separation, failure visibility, recovery steps, and documented limitations. The engagement defines who owns infrastructure, model-provider accounts, source updates, user support, and approval of future changes. Security controls are scoped to the actual environment; no blanket compliance claim is implied.
Evaluation before and after deployment
An evaluation set represents ordinary requests, edge cases, unsupported questions, invalid inputs, and dependency failures. Measures are selected for the task: grounded answer quality, extraction correctness, appropriate refusal, tool selection, approval behavior, and complete workflow execution.
Evaluation compares versions and identifies regressions. A useful result includes failure analysis, not only an average score. The team agrees acceptance criteria before release and determines which changes require a new review.
Monitoring, optimization and scale
Production monitoring should make model errors, tool failures, retrieval gaps, latency, resource use, and unresolved handoffs visible. Logs follow the agreed privacy and retention policy. Model or prompt changes are tested against the evaluation set before broader rollout.
Scale follows evidence from the first release. Additional users, departments, knowledge collections, integrations, and tasks may need new permissions, capacity planning, and support ownership. Optimization can improve retrieval, workflow design, model selection, or conventional code; adding a larger model is not the only option.
Technology, industries and engagement models
I-360's documented engineering work includes .NET, ASP.NET Core, SQL Server, APIs, local Ollama integration, knowledge retrieval, healthcare interfaces, and business applications. The selected technology stack depends on requirements and the existing environment.
Engagements can begin with an assessment, a scoped pilot, an implementation phase, or an ongoing engineering arrangement. Healthcare, finance operations, professional services, and logistics present different data and review needs. We define these dependencies in the proposal rather than treating one implementation template as suitable for every industry.
What your proposal and handover should contain
A proposal identifies the selected use case, supported users, deliverables, excluded features, source systems, client inputs, acceptance criteria, milestones, and commercial terms. The final scope may include application code, integration specifications, evaluation evidence, deployment instructions, and operational documentation.
Bring your process, sample inputs, existing systems, data restrictions, target environment, and the result your team needs to review. Ongoing monitoring, model changes, knowledge updates, and additional features are separately agreed. This gives you a concrete basis for selecting an AI implementation company and reviewing the delivered work.
Questions before you start
Can you implement an existing AI strategy?
Yes. We review the proposed use case, architecture assumptions, source access, dependencies and acceptance criteria before defining the engineering scope.
Do we need a prototype first?
A prototype is useful when it resolves a material uncertainty. It is scoped around a specific question and is not a substitute for production integration, controls or testing.
Can deployment remain on our infrastructure?
Local deployment can be assessed against model suitability, hardware, network constraints, operating responsibilities and the required data policy.
How are timelines and costs determined?
They depend on the selected workflow, integrations, data readiness, infrastructure and acceptance requirements. A proposal follows discovery; this page does not promise a fixed price or duration.
Related services and product examples
Define a useful next step.
Share the current workflow, systems, constraints, and result you want to review. We will discuss an appropriate scope and the inputs it needs.