AI Agent Development
Goal-driven agents that reason, use tools, execute multi-step work and escalate when human judgment is needed.
Explore AI agent developmentAI ENGINEERING SERVICES
Build AI that works inside your business.
From intelligent copilots to autonomous workflows, Koddox designs and deploys production-grade AI systems around your data, tools and operating reality.
AI Engineering
Move beyond experiments. We turn valuable AI opportunities into secure, integrated systems that deliver measurable business outcomes.
AI Engineering capabilities
Agentic AI Systems
Multi-agent architecture, orchestration, memory, permissions, evaluations and operational guardrails.
Discuss this capabilityGenerative AI
Custom LLM applications, copilots, content intelligence and multimodal customer or employee experiences.
Discuss this capabilityRAG & Knowledge AI
Grounded answers across documents, databases and enterprise knowledge, with citations and access controls.
Discuss this capabilityConversational AI
Customer and internal assistants that understand context, connect to systems and complete real work.
Discuss this capabilityPredictive AI & ML
Forecasting, classification, recommendations, anomaly detection and decision-support models.
Discuss this capabilityCustom AI development, from workflow discovery to deployment
Start with a workflow worth improving
A useful AI engagement begins with a specific task: finding answers in internal documents, extracting information from incoming files, preparing customer responses or coordinating work across systems. We map the users, data sources, decisions and exceptions before recommending a model or agent architecture.
Discovery should produce a prioritized use-case list, data-access requirements, a baseline for current performance and an initial delivery scope. Where a rules-based integration solves the problem reliably, it can remain part of the solution.
Choose the right level of autonomy
A knowledge assistant retrieves information and drafts answers. A tool-using agent can also query an approved system or prepare an action. A workflow agent coordinates several steps, with explicit permissions and handoffs. We scope that authority around the consequences of an incorrect action.
For sensitive workflows, the initial release can remain read-only or prepare changes for a person to approve. Payment execution, account changes and external communications need separately defined authorization. Human review is a designed step in the workflow.
Connect AI to your existing software
Useful AI needs reliable access to the systems where work happens. Integration scope can include your CRM, ERP, help desk, accounting platform, document repository and internal APIs. We define source ownership, authentication, data freshness and failure handling for each connection.
For retrieval-augmented generation, the design also addresses document ingestion, metadata, access filtering, citations and update frequency. A user should only receive information they are permitted to access, even when a model can search across multiple sources.
Evaluate quality before expanding usage
A prototype demonstrates a possibility; a release needs evidence that it handles representative work. We agree an evaluation set that includes ordinary requests, ambiguous requests, missing information, tool failures and attempts to exceed permissions.
Acceptance measures can include answer correctness, citation support, task completion, escalation rate, response latency and cost per completed task. The target values depend on your workflow and are agreed during discovery rather than assumed in advance.
What an engagement can deliver
A scoped engagement can include a workflow specification, architecture, working integrations, an evaluation dataset, application code, deployment configuration and an operating guide. Delivery milestones should make each of these reviewable as the system develops.
After release, monitoring and scheduled reviews help identify quality regressions, failed integrations and changing usage patterns. Model or prompt changes should be tested against the agreed evaluation set before wider rollout.
Before we build.
Can you add AI to software we already use?
Yes. The first step is to confirm API access, authentication, data quality and the actions your existing systems support. We then scope the integration and any application changes required.
Do we need to train a new model?
Not necessarily. An existing model combined with retrieval, tools and evaluation may be sufficient. Fine-tuning or custom machine learning becomes a separate decision when the task and available data justify it.
How will we judge whether the project works?
We agree acceptance criteria before implementation. These can combine task accuracy, review effort, completion time, operating cost and security tests using representative examples from your workflow.
Can an AI agent operate without approval?
Only within an explicitly agreed scope. The design can require approval for sensitive actions, limit tool permissions and route uncertainty to a person.