02 / Services
AI Systems
An AI feature is easy to prototype and hard to operate. We build retrieval, reasoning, and automation into real products, with evaluation, cost control, and failure handling designed in from the start.
When this applies
Reasons companies call us about this.
If more than one of these is true, this is probably the right starting point.
- You have an AI prototype that cannot be trusted in front of customers
- Your team answers the same questions out of scattered documents
- You want AI inside the product, not bolted beside it
- A manual review process is the bottleneck
Scope
What the engagement includes.
Scope is written down and approved before engineering begins. Anything outside it is quoted, not assumed.
Deliverables
- Use case definition and feasibility review
- Retrieval and data pipeline design
- Model selection and prompt architecture
- Evaluation harness and quality benchmarks
- Cost, latency, and rate-limit engineering
- Monitoring, logging, and fallback behavior
Technology
- LLM APIs and open models
- RAG and vector search
- Embeddings and reranking
- Document and ETL pipelines
- Evaluation tooling
- Streaming interfaces
How we work
The phases for this work.
- 01
Discover
Understand the company, the users, the business model, the technical environment, and the objective.
- 02
Define
Set product scope, architecture, priorities, and the technical requirements that follow from them.
- 03
Design
Build the user experience, interface system, prototypes, and product flows.
- 04
Build
Engineer the application on an architecture meant to survive its second year.
- 05
Validate
Test functionality, responsive behavior, performance, accessibility, and integrations.
- 06
Launch
Deploy, instrument, monitor, and support the product in production.
Related
Capabilities that usually pair with this.
Start a project
Have a ai systems project?
Send the problem, the constraints, and the timeline. We will come back with scope, approach, and a number.
Custom product engagements typically begin at $10k.