Applied AI and data work, scoped to the decision at hand
We focus on a small set of services we can do well. Each engagement is shaped around a specific decision or workflow, and each one ends with documented, maintainable work your team can run. Representative deliverables are listed below — actual outputs are tailored to your context.
We work with teams to identify where language models and automation add real value, then design end-to-end workflows that fit existing tools and review steps. The emphasis is on reliability, clear handoffs, and keeping a human in control of consequential decisions.
Representative deliverables
Workflow maps and process diagrams
Tool and integration recommendations
Human-in-the-loop checkpoints
Implementation and rollout notes
02
LLM Evaluation & Prompt Systems
We build reusable prompt libraries and evaluation frameworks that make model behaviour testable. Instead of one-off prompts, you get a maintainable system with defined criteria, test sets, and a way to compare changes before they ship.
Representative deliverables
Prompt libraries and templates
Evaluation rubrics and scoring criteria
Test sets and regression checks
Comparison reports across model versions
03
Data Analytics & Dashboards
We help teams consolidate data and build analytics and dashboards that answer the questions decisions actually depend on. The goal is clarity and trustworthiness — well-defined metrics, documented sources, and visuals that hold up to scrutiny.
Representative deliverables
Dashboards and reporting views
Metric definitions and data dictionaries
Data preparation and cleaning scripts
Source documentation and refresh notes
04
Research & Evaluation Support
We support research and evaluation work — framing questions, structuring analysis, and synthesizing findings into clear, well-documented memos. The emphasis is on transparent methods, stated assumptions, and honest treatment of limitations.
Representative deliverables
Research and evaluation memos
Method write-ups and assumptions logs
Literature and source summaries
Findings briefs for decision-makers
05
Responsible AI Documentation
We produce the documentation that responsible AI use depends on: how a system works, how it was tested, what its limitations are, and where human oversight applies. This makes systems easier to review, maintain, and govern over time.
Representative deliverables
System and model documentation
AI risk and limitation notes
Testing and oversight records
Usage guidance and review checklists
06
Process Automation
We automate repetitive, well-understood tasks so people can focus on judgment-heavy work. Automations are built to be observable and reversible, with logging, error handling, and clear points where a person stays in the loop.
Representative deliverables
Automation scripts and integrations
Logging and error-handling setup
Run-books and operating guidance
Maintenance and handover documentation
Don't see exactly what you need? These services often combine — for example,
an evaluation framework feeding a dashboard, or a workflow paired with the
documentation to govern it. Reach out and describe your situation.
Work with Northstar
Let's scope the right piece of work
Share the decision or workflow you're focused on, and we'll suggest a focused engagement with clear deliverables.