Hallucinations in production
We ground answers in your content, cite sources where it matters, and fail closed when confidence is low.
Service
AI & Machine Learning
We integrate intelligent features—search, recommendations, document automation, and predictive workflows—grounded in your data and business rules.
Talk about this serviceHow we approach it
Most “AI projects” fail because they start with a model instead of a job. We start with a workflow: which documents, tickets, or searches waste the most time, what a good answer looks like, and what must never be automated without a human.
Then we wire models, retrieval, and your existing systems so the feature is testable, logged, and cheap enough to run. Demos are easy. Production with guardrails is the work.
Who this is for
- Teams drowning in documents, support mail, or unstructured files
- Products that need search, summaries, or recommendations that are actually relevant
- Operators who want automation with an audit trail, not a black box
- Leaders who have tried a chatbot and need something that respects their data
Problems we take on
Typical starting points — not a menu you have to pick from. Most engagements mix two or three of these.
Data you cannot send to a public API
We design around your privacy rules: what stays private, what is redacted, and what can be logged.
A prototype nobody trusts
Evaluation sets, human review queues, and metrics beat a single impressive demo.
How an engagement runs
Pick a job
One workflow, one success metric. We will not boil the ocean with a “company-wide AI platform” as step one.
Prove it on real data
A thin slice on your documents or tickets, scored by people who know the domain.
Harden
Auth, PII, rate limits, fallbacks, and monitoring. Then we put it in the product.
Iterate
Wrong answers become training and prompt material. The system should get less surprising over time.
What you walk away with
- Use-case workshop and a go / no-go on feasibility
- Data map: sources, quality, and access rules
- Working pipeline (RAG, classification, extraction, or recommendations)
- UI or API integration into the tools people already open
- Evaluation set, logging, and a review process
- Cost and latency estimates for real usage
Tools we use
We pick for the job and for the team who will own it later — not a default stack for every client.
PythonOpenAI / compatible APIsRAG pipelinesvector searchNext.jsworkflow automation
- ▸Use-case discovery and feasibility
- ▸Model integration and RAG pipelines
- ▸Workflow automation with guardrails
- ▸Evaluation, monitoring, and iteration
Questions we hear first
Do we need our own model?
Almost never at the start. We use strong hosted models plus your data. Custom training is a later conversation if the numbers justify it.
Will this replace our team?
We design for assistance and review, not silent full automation, unless you explicitly want a narrow, well-tested task fully automated.
How do you measure success?
Time saved, accuracy against a labeled set, or conversion on a recommendation — agreed before we build.
Want this scoped for your team?
Send a short brief. We will come back with questions, a suggested shape of work, and whether we are the right fit.