AI Developers Who Ship Features You Can Measure
Engineers who treat a model as one component in a system. They build the retrieval, the tests and the monitoring around it, in your repository and your tools.
You interview the developer before any contract.
Three Ways To Work With Us
Pick the one that matches where you are. You can move between them as the work changes.
You have a use case and want it in production. Our developers join your team and build the feature end to end: data preparation, retrieval, prompts, model calls, evaluation and deployment. You set priorities and review the work.
- Feature delivery
- Your repository
- Your sprint
You are not sure what to build, or whether AI is the right tool. A senior engineer reviews your data, your constraints and your options, then writes down a recommendation with the trade-offs. Sometimes the recommendation is a simpler approach with no model in it.
- Feasibility review
- Architecture options
- Written recommendation
You have an AI prototype or an older machine learning system that is hard to change. We add tests, evaluation and monitoring, replace fragile parts, and document how it works so your team can maintain it.
- Prototype hardening
- Evaluation added
- Documentation
The AI Stack Our Developers Work In
Tools change quickly. The layers stay the same. We match developers to the layers you need and the tools you already use.
Model APIs
Hosted models called over an API, and open-weight models you run yourself.
- Hosted APIs from the major model providers
- Open-weight models such as Llama and Mistral
- Hugging Face Transformers
- Ollama and vLLM for self-hosted inference
Retrieval
Finding the right source material before the model answers.
- Vector search: pgvector, Qdrant, Weaviate, Milvus
- Keyword and hybrid search: Elasticsearch, OpenSearch
- Document parsing and chunking
- Embedding models and rerankers
Orchestration
Connecting model calls, tools and business logic into a workflow.
- LangChain and LangGraph
- LlamaIndex
- Haystack
- Plain application code where a framework adds nothing
Evaluation
Checking output quality with repeatable tests, not impressions.
- Test sets built from real examples
- Open tools such as Ragas, promptfoo and DeepEval
- Experiment tracking with MLflow
- Human review for cases a metric cannot judge
Deployment
Running the feature in production with the same discipline as any other service.
- Docker and Kubernetes
- Python services with FastAPI
- Tracing with OpenTelemetry
- Cost, latency and error monitoring
Data Foundations
Most AI work is data work. Clean, permissioned, current data comes first.
- Ingestion and transformation pipelines
- PostgreSQL and data warehouses
- Access control carried through to retrieval
- Refresh schedules and versioning
Tool names are examples of what our developers work with. They are not endorsements or partnerships.
Build Or Advise: Which Do You Need?
Both start with a conversation. They differ in what you receive at the end.
Build
- You haveA defined use case, data you can access and someone who can review the work.
- We provideDevelopers who join your team and write production code in your repository.
- You receiveWorking software, tests, an evaluation set and documentation.
- Commercial modelMonthly per engineer, on month-to-month terms.
Advise
- You haveA problem, a hunch that AI may help and open questions about cost or risk.
- We provideA senior engineer who studies the problem and tests the riskiest assumption.
- You receiveA written recommendation with options, trade-offs and a suggested first step.
- Commercial modelA scoped piece of work agreed in writing before it starts.
Not sure which applies? Send the brief and we will tell you which we would choose and why.
Tell Us What You Need
Common problems we hear, and how we would usually approach each one.
Support staff answer the same questions every day
An assistant that retrieves answers from your own help content and cites the source. It hands over to a person when it is not confident.
Staff spend hours reading documents to find a few fields
Document extraction with a fixed output schema, validation rules and a review queue for uncertain results.
Nobody can find anything in the internal knowledge base
Hybrid search across your documents that respects existing access permissions, with answers linked to the original page.
Our prototype works in the demo and fails with real users
An evaluation set built from real inputs, then targeted fixes to retrieval, prompts and error handling until the failure cases pass.
Model costs are growing faster than usage
Measure cost per request first. Then apply caching, smaller models for simple tasks, shorter prompts and batch processing where the use case allows.
We cannot send customer data to a third party
Options include self-hosted open-weight models, redaction before any external call, or a provider agreement your security team has approved. We work within your policy.
We want an agent to complete multi-step tasks
Start with a narrow workflow, a small set of tools and clear permission limits. Add human approval for any action that is hard to undo.
We have an old machine learning model nobody understands
Document what it does, reproduce its results, add monitoring for drift, then decide whether to retrain, replace or retire it.
Working Standards
These are habits we expect from every AI developer we place. They are working practices, not certifications.
Evaluation before launch
Every feature has a test set and a pass threshold agreed with you before it reaches users.
Traceable answers
Where the feature uses your content, responses point back to the source so a person can check them.
Data handled by your rules
Developers follow your policies on what data may be sent to which service. An NDA is signed before code access.
A person in the loop
Actions that are costly or hard to reverse need human approval until you decide otherwise.
Prompts are code
Prompts and configuration live in version control and go through review like any other change.
Honest limits
Language models can produce wrong answers that sound right. We design for that and say so plainly.
Engagement Models
- Individual
One AI Developer
A single engineer who joins your team and takes direction from your lead. Suits a team that already has a plan.
- Team
Dedicated AI Team
A small team covering data, application and evaluation work, focused on one product area you define.
- Scoped
Advisory Review
A senior engineer reviews a use case or an existing system and writes up findings and options.
Developer engagements run month to month with no exit fee. If the fit is wrong, we replace the developer.
Send Us Your Brief
A few lines is enough. Tell us the problem, the data you have and what a good result looks like.
Helpful to include
- The problem in plain words
- Where the data lives
- Any limits on data sharing
- Who will review the work
What you receive
- Our view on build or advise
- A suggested team shape
- Profiles to review and interview
Related Insights
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- ModernisationSigns your legacy platform has become a business riskWhat to look for in release frequency, incident history and hiring difficulty.
Frequently Asked Questions
Much of the work overlaps. An AI developer also knows how to prepare data for retrieval, write and test prompts, measure output quality, and handle the ways a model can fail. Most production AI features need both skill sets.
Yes. You interview every candidate before any contract. You can set a technical task if you wish, and you make the final decision.
You do. All code and intellectual property created for you is assigned to you in the contract. Work happens in your repository and your tools.
Usually not. Many use cases are served well by an existing model combined with retrieval over your own content. Fine-tuning or training is worth considering when you have enough quality data and a task that existing models handle poorly. We will tell you which applies.
Tell us. We first try to fix the problem. If that does not work, we replace the developer. Terms are month to month and there is no exit fee.