AI Integrations
Practical artificial intelligence features added to the systems your team already uses, with human review where it matters.
Artificial intelligence is most useful when it is connected to your own information and embedded in a process someone already owns. We build AI features into existing business applications rather than delivering a separate tool that nobody opens.
Every integration is designed with a defined scope, a defined data source, and a defined review step. We are specific about what the system can do, what it cannot do, and where a person needs to stay in the loop.
What we build
AI assistants
Conversational or task-specific assistants scoped to a defined body of knowledge — internal policies, product documentation, historical tickets — with permissions that respect who is allowed to see what.
Knowledge-base integrations
Retrieval over your own content so answers cite the underlying source. Staff can check the original document instead of trusting a summary on faith.
Document-processing workflows
Intake of PDFs, scans, forms, and email attachments; extraction of the fields you care about; validation against your existing records; and routing into the next step of the process.
AI-enabled internal tools
Drafting, summarizing, and lookup features placed directly inside the applications your team already works in, so the AI step does not require switching context.
Customer-service automation
Suggested replies, request summarization, and routing to the right queue or person, with configurable thresholds for what is handled automatically and what is escalated.
Data classification
Tagging and categorizing records, tickets, documents, or transactions against your own taxonomy, with confidence thresholds and a review queue for uncertain cases.
AI workflow orchestration
Multi-step processes that combine model calls with database lookups, business rules, approvals, and notifications — with logging at each step so results can be traced.
Integration with existing applications
Connections to the CRM, ticketing system, file storage, or line-of-business software you already run, so AI output lands where the work happens.
How we scope an AI project
- Identify one process with a measurable, agreed definition of "working"
- Confirm what data the system may use, where it lives, and who may see it
- Define the review step: what a person checks, and when
- Build a limited version and evaluate it against real examples from your organization
- Decide, with evidence, whether to expand, adjust, or stop
Data handling
Before implementation we agree in writing on which data may be sent to which providers, what retention settings apply, and which categories of information are excluded entirely. Model providers, hosting regions, and configuration options are chosen to fit those constraints, and are documented for your records.
What you receive
- Written scope covering data sources, permissions, and review steps
- Evaluation results against examples drawn from your own records
- Working integration inside your existing systems
- Logging and monitoring for AI-assisted steps
- Documentation for operators and reviewers
What to know before starting
What AI can and cannot do here
People and process
Not professional advice
Often combined with
Business Automation
Removing repetitive manual steps from the processes your team runs every day.
Read moreCustom Software Development
Applications designed around your requirements, your data, and the way your organization actually works.
Read moreAPI and Systems Integration
Connecting the systems you already run so information moves reliably between them.
Read more
Not sure which of these you need? Request a consultation and describe the problem in your own terms.