AI services & engineering
Build the AI capability your business needs.
From agents and automation to private AI infrastructure, we help you choose, build, evaluate and deploy a practical solution.
01 / What we build
Custom AI Agents
Turn incoming requests into work ready for your review.
Explore the scope & deliverables
- The common problem
- A recurring task depends on someone reading, deciding, and moving information between tools, and the variations are too many for a fixed script or rules engine.
- What we can build
- Agents that gather context from approved sources, call the tools you allow, and produce a draft, recommendation, or action. Approval points are designed in wherever a person should stay in the decision.
- Example deliverables
- Workflow map with scope, inputs, and success criteria
- Working agent prototype connected to agreed tools and test data
- Evaluation set of representative examples with review notes
- Permissions, escalation rules, and logging for agent actions
- Deployment notes and a maintainable handoff
- How a project typically begins
- A short discovery conversation about one workflow: who does it today, what arrives, what a good result looks like, and where mistakes are costly.
02 / What we build
Workflow Automation
Move information between your tools, with fewer manual handoffs.
Explore the scope & deliverables
- The common problem
- Recurring processes such as intake, routing, reconciliation, and reporting still run through email threads, spreadsheets, and copy-and-paste between web tools.
- What we can build
- Automations that connect your existing systems, extract and validate data from documents and messages, and route work to the right person. Deterministic steps stay deterministic; AI handles the parts that need interpretation.
- Example deliverables
- Process map with automation candidates ranked by value and risk
- Implemented automation across your existing tools
- Exception queue and error handling so nothing fails silently
- Run logs so each outcome can be traced and reviewed
- Handoff documentation for your team
- How a project typically begins
- We walk through one process as it runs today and review a few redacted, real examples of its inputs and outputs.
03 / What we build
Knowledge Agents
Ask your own documents. Check the sources behind each answer.
Explore the scope & deliverables
- The common problem
- Policies, contracts, manuals, tickets, and past project files sit in many places, and people lose time searching or rely on whoever happens to remember.
- What we can build
- Agents that search a defined document set, answer with references to the source passages, and help compare, summarize, or draft reports. Access boundaries follow the permissions you set.
- Example deliverables
- Source inventory and access plan
- Retrieval prototype over a bounded document set
- Test set of real questions, with checks on answer quality and citations
- Guidance for keeping source content current
- Notes on where data is stored and processed
- How a project typically begins
- We agree on a bounded set of documents and a starter list of the real questions your team asks.
04 / What we build
Computer Vision AI Agents
OptivizVision: our computer vision AI agent, built to find a production fit for your visual task.
Explore the scope & deliverables
- The common problem
- Teams need to count, check, measure, or read things in images and video, such as inspection photos, scans, and site imagery, and manual review is slow or inconsistent.
- What we can build
- OptivizVision brings together task understanding, catalog-based model and tool selection, pipeline execution and evidence review. We adapt vision workflows for production requirements across detection, segmentation, OCR, inspection and other task families, using your samples to evaluate fit.
- Example deliverables
- Task definition and success criteria
- Review of representative sample images or video
- Prototype pipeline with annotated outputs
- Evaluation report on your own samples, including failure cases
- Deployment options based on your privacy and infrastructure needs
- How a project typically begins
- We look at a few representative, non-sensitive images or clips and define the decision the results need to support.
05 / What we build
Local & Private AI Deployment
Deploy models and agents on your own hardware or private cloud, with integration and operating support.
Explore the scope & deliverables
- The common problem
- You need AI capabilities close to your data, with clear infrastructure, access and operating boundaries.
- What we can build
- Model selection, GPU and capacity sizing, inference serving, application integration, access controls and monitoring on your own hardware or private cloud.
- Example deliverables
- Model and runtime recommendation matched to your workload
- Hardware sizing and capacity plan
- Configured inference service and application endpoints
- Access controls, monitoring and deployment checks
- Operating runbook and team handoff
- How a project typically begins
- We review the use case, data boundaries, existing hardware and expected load, then define a practical first deployment.
Our approach
A clear next step. At every stage.
Agree on the scope. Try a prototype. Validate before deployment.
Discovery
We map the workflow, the data and tools involved, the risks, and what a good result looks like.
Focused prototype
We build the smallest working version that exercises the core loop on realistic examples.
Validation
We test against representative cases, review failures with you, and agree where human review is required.
Deployment and handoff
We connect the approved systems, add review checkpoints, and document how to run and maintain the solution.
Start with one workflow.
Tell us where the work gets stuck. We'll help you choose a practical first project.