OpenAI and LLM application development with the controls a business needs.
Put language models to work reading documents, routing requests, and drafting replies inside the software your team already uses, with answers drawn from approved records, uncertain cases sent to a person, and the model chosen per task.
- InterfaceReact · Next.js
- MobileReact Native
- ServicesNode.js · TypeScript
- DataPostgreSQL · Python pipelines
- AIOpenAI · Anthropic · chosen per task
- CloudAWS · Azure · Google Cloud
What we build with it
Where OpenAI and LLM application development fits.
Document extraction
Fields from invoices, forms, and contracts entered into the system of record, with uncertain results sent for review.
Classification and routing
Requests sorted and sent to the right queue with the reason shown.
Drafting and summarizing
Replies, summaries, and notes for a person to approve.
Retrieval-based assistants
Answers from your own documents and records, with the source shown and nothing invented.
Use cases
Applications we build with OpenAI and LLM application development.
Contract and clause review
Contracts compared against the company’s standard positions, with differences flagged and explained for a lawyer or manager to review.
Support agent assistance
Suggested answers, relevant policies, and customer history shown to support staff beside each ticket, so the agent decides what to send.
Plain-language reporting questions
Staff ask questions about operational data in plain language and receive answers built from approved queries, with the underlying figures shown.
Catalog and record enrichment
Product descriptions, categories, and attributes filled in or standardized across large catalogs, with changes queued for approval before publishing.
Quality and policy checks
Case notes, claims, or orders checked against written rules, with likely gaps flagged for a reviewer before the record moves on.
Translation inside workflows
Customer messages, instructions, and forms translated between languages inside existing workflows, with terminology lists and human review for anything sensitive.
We choose it when
- The task is repeated, has representative data, and has a clear definition of correct.
- The output can be reviewed where it matters and measured after launch.
- The data boundaries can be agreed on and enforced: what the model sees and where it runs.
We reach for something else when
- Novel judgment calls with no definition of correct: keep them with people.
- A vendor feature already does the task with acceptable data terms: turn it on.
- Data that cannot leave your systems, with no provider able to run inside them: a smaller model run inside your environment, or no model at all.
How we use it
We choose the provider and model per task (OpenAI, Anthropic, or a model run inside your environment), retrieve from your records with permissions carried through, keep prompts and evaluation in code tested on your cases, route low-confidence results to review, and monitor accuracy, volume, and cost. We build in Python and TypeScript inside the application people already use.
What it means for you
- Prompts and evaluation sets live in code, tested on your cases like the rest of the application.
- Switching providers or models is a configuration change, not a rebuild.
- Data boundaries are written down before anything is built.
Engineering practice
How we build with OpenAI and LLM application development.
Evaluation sets from real cases
Each feature is measured against a set of representative, labeled examples before launch and again after every prompt or model change.
Permission-aware retrieval
Retrieval checks each person’s access before a document reaches the model, so an assistant can only draw on records that person may see.
Prompt injection defenses
Content from emails, documents, and web pages is treated as data, with model actions limited to approved tools and checked before anything is changed.
Human review thresholds
Low-confidence results go to a review queue, and reviewer corrections are recorded so the evaluation set grows from real mistakes.
Usage and quality monitoring
Requests, answer quality, errors, and provider usage are tracked per feature, with alerts when accuracy drops or volume moves outside expected ranges.
Questions
Questions about OpenAI and LLM application development.
What business and technology leaders ask us about it.
Which provider do you use?
The one that fits the task, the data boundaries, and the cost. We use OpenAI and Anthropic models for most tasks, and a model run inside your environment where the data requires it.
How do you stop it inventing things?
Assistants answer only from your records and show the source; extraction and routing send uncertain cases to a person. Behavior is measured on your cases before rollout.
Where does our data go?
We agree on it in writing first: what the model may see, where it is processed, and what never leaves your systems.
Do you train models on our data?
No. We use provider models with retrieval from your records. Fine-tuning is a separate decision and rarely the first step.
Are LLM applications ready for enterprise use?
For well-defined tasks, yes, when they are built with controls: agreed data boundaries, retrieval from approved records, permissions carried through, evaluation on real cases, and review of uncertain results. Business terms from the major providers cover data retention and training use. Open-ended decisions with no definition of correct are still better left with people.
What happens when a provider changes or retires a model?
Prompts, model choices, and evaluation sets live in code, so a new model version is run against the same cases before it replaces the old one. If results change, the prompt or configuration is adjusted and retested. Switching providers follows the same path, which keeps a model change a routine release rather than a rebuild.
What drives the running cost of an LLM feature?
Mostly the volume of requests, the length of the documents and context sent with each one, and the model chosen. Smaller models handle many routine tasks well, caching avoids repeating identical work, and retrieval sends only the relevant passages. Cost is monitored per feature from launch, so changes in usage are visible.
Discuss an AI use case.
Tell us about the application and the systems around it.We’ll come back with what we would build first, and which tools we would use.