AI ENGINEERING
Custom AI systems.
Production engineering.
Measurable outcomes.
Bring us the work, the constraints and the systems it touches. We’ll define a useful first release and the engineering needed to make it dependable.
Discuss an AI project
FORWARD-DEPLOYED ENGINEERING
Our engineers,
working alongside your team.
We work with your business and technical teams to understand the workflow, build the integrations and take the system into production.
Each engagement has agreed deliverables, acceptance criteria and operational ownership, supported by Agentscy products and reusable integrations.
Discuss a project 01 / ENGAGEMENTEnterprise agents & automation
Work gets stuck between conversations, documents and systems.
Discuss a project - What we can build
- Knowledge assistants, internal copilots and agents that use approved tools to move a workflow forward.
- How we approach it
- Retrieval, state, API or MCP integrations, browser workflows where appropriate, and human approval boundaries.
- Example deliverable
- A lead-handling workflow that gathers missing context, updates your system and hands off exceptions.
02 / ENGAGEMENTAI-native software
A model alone does not give your team a usable product.
Discuss a project - What we can build
- Custom AI products, SaaS applications, backends and AI capabilities within an existing application.
- How we approach it
- Product discovery, application and platform engineering, integration design and incremental modernization.
- Example deliverable
- A working application with an evaluated AI feature, access controls and a documented deployment path.
03 / ENGAGEMENTAI infrastructure & inference
The right model choice depends on the workload and where data may be processed.
Discuss a project - What we can build
- Model deployment, inference benchmarking, small-model serving and private-deployment feasibility studies.
- How we approach it
- Compare quality, latency and economics on your examples. Scope hosting and operational responsibility before a build.
- Example deliverable
- A benchmark and deployment recommendation, followed by an agreed implementation. Private GPU operations and fine-tuning are scoped work.
04 / ENGAGEMENTAI evaluation & reliability
A convincing demo does not tell you how a system handles the difficult cases.
Discuss a project - What we can build
- Scenario suites, golden datasets, tracing, monitoring and regression checks around your agent or application.
- How we approach it
- Define success, test failure and escalation paths, inspect execution evidence and compare changes against a baseline.
- Example deliverable
- A repeatable evaluation harness and a review report covering completion, exceptions, latency and cost.
THE RIGHT FIT FOR THE TASK
Choose the model
around the work.
Balance quality, response time, cost and data requirements. Establish the trade-offs before committing to a deployment.
EXPLORE A REQUIREMENT
ILLUSTRATIVE DEPLOYMENT CHOICEEvaluate a smaller model
Benchmark a focused model against your examples. Compare quality, latency and cost before choosing.
Architecture concept. Private hosting, small-model serving and fine-tuning are assessed and scoped per engagement.
Start with a problem worth solving.
We’ll agree on feasibility, scope and acceptance criteria before the build.
Discuss an AI project