Services · AI Implementation
Implement AI in production—with the data, integrations, controls, and ownership it needs.
Implementation, not a demo
The model is one component. The business system is the implementation.
A dependable implementation includes the workflow, approved context, integrations, identity and permissions, evaluation, human approvals, observability, incident response, adoption, and an operating owner. Model independence is a design goal—not a promise that every model behaves identically.
Buy
Use an existing product when the workflow is standard, controls are acceptable, integration is sufficient, and vendor economics fit.
Configure
Adapt an existing system when most of the capability exists but business rules, permissions, routing, or reporting need careful setup.
Integrate
Connect current systems when the primary constraint is fragmented data, handoffs, or missing workflow events rather than absent software.
Build
Create a custom agent or application when the workflow, context, control requirements, or strategic value cannot be met responsibly with available products.
Production path
From business objective to governed operation.
The order prevents a persuasive prototype from bypassing integration, safety, adoption, or ownership work. Each stage produces evidence for the next decision.
Define the production job
Assess data and integration readiness
Choose buy, configure, integrate, or build
Design controls and evaluation
Deploy through shadow and assisted modes
Operate and improve
Reference architecture
Seven layers that make an AI capability operable.
The exact technology varies. The responsibility for data, decisions, permissions, evidence, failure, and maintenance does not disappear.
Layer 1
Workflow and decision map
Current steps, triggers, owners, exceptions, approval authority, baseline, and target state.
Layer 2
Company context and data contract
Approved sources, systems of record, ownership, freshness, retention, citations, and minimum-necessary access.
Layer 3
Integration and tool layer
Permissioned APIs and workflow tools for CRM, files, databases, accounting, operations, communication, and internal software.
Layer 4
Model and orchestration layer
A replaceable component selected for the task. Changing a model can require regression evaluation, prompt or tool adjustments, safety review, and staged rollout.
Layer 5
Evaluation and guardrails
Representative test sets, prohibited outcomes, structured validation, human approvals, fallback behavior, and rollback criteria.
Layer 6
Observability and incident response
Logs, traces, quality signals, cost and latency monitoring, alerts, runbooks, escalation owners, and post-incident review.
Layer 7
Adoption and operating ownership
Role-specific training, documented procedures, change control, support boundaries, and the team responsible after launch.
Cost and timeline
The model bill is rarely the whole implementation cost.
Scope changes with workflow breadth, source quality, system access, custom integration, identity and security requirements, exception volume, evaluation depth, user training, support coverage, and third-party licenses or usage.
A diagnostic identifies the bounded outcome and major dependencies. A Blueprint is appropriate when the business needs a deeper architecture, business case, sequencing plan, and implementation scope before committing to a build.
Questions
Direct implementation answers.
What does an AI implementation company do?
It converts a business objective into a production system: mapping the workflow, deciding what to buy or build, preparing data and integrations, defining permissions and human approvals, testing quality and failure behavior, deploying, training owners, and establishing ongoing monitoring.
What data is required?
Only the approved data and system context needed for the bounded job. Each source should have an owner, permission rule, freshness expectation, retention policy, and system-of-record status. Unrestricted access to the whole company is neither required nor appropriate.
How long does implementation take?
The timeline depends on workflow scope, source quality, integration access, security review, exception complexity, user availability, and acceptance testing. Krastor scopes these dependencies before committing to a delivery plan.
Can you replace the model later?
The architecture can reduce model lock-in, but changing models is not always a one-line switch. Tool calling, prompts, latency, cost, output quality, safety behavior, and evaluations may differ. A model change should pass regression tests and a controlled rollout.
How are AI agents controlled?
Agents receive only approved tools and data, with task-level permissions, deterministic limits, human approval for consequential actions, complete activity logs, failure fallbacks, and an owner who can pause or roll back the workflow.
Who owns what gets built?
Ownership is defined in the scope. Krastor's default direction is client-controlled accounts, data, documentation, and deployable artifacts, with third-party licenses and platform restrictions disclosed before implementation.
Engagement starts here
Start with the diagnostic.
Thirty minutes. We map your operation, name what's actually slowing it down, and tell you what we'd do if we were running it. You get a written stack assessment after the call, whether you hire us or not.
Not limited to what's listed. Every engagement starts by assessing what your business actually needs, and we build whatever it requires.