Pushbuild
← All services

Language models inside the software you already use.

We add OpenAI, Claude or open models to an existing app, database or workflow for specific jobs: summarising, classifying, extracting fields, drafting replies, or answering questions from your documents. Each feature gets a test set, cost limits and a log, so you know how well it works and what it costs.

Tell us what you needSee typical projects

A written scope before we start, so you know what you're getting before work begins.

EmailsFieldsDocumentsAnswersRecordsReview queueLLM featurebuilt by Pushbuild

Sound familiar?

What we hear from businesses before a project like this starts.

  • We want AI in our product but don't know which feature is worth building first.
  • A prototype worked in a demo and gives wrong answers on real data.
  • API costs are unpredictable and nobody is watching them.
  • Staff paste customer data into ChatGPT by hand to get the same task done.
  • We need answers grounded in our own documents, not the model's guesses.

What buyers usually need

The requests that come up most often for this kind of work, and what each one takes to work properly.

01

One feature, measured

A specific job with a test set of real examples and a pass rate agreed up front.

02

Grounded answers

Retrieval over your documents or database, with sources shown.

03

Structured output

Fields extracted into your schema and validated before they're saved.

04

Cost and safety controls

Budgets, rate limits, caching, logging and redaction of sensitive fields.

05

Human review where it matters

Low-confidence results routed to a person instead of saved silently.

Tools that come up most: OpenAI API, Anthropic Claude API, Azure OpenAI, open-weight models, embeddings and pgvector, LangChain or plain SDKs, Python, Node.js and your existing stack.

What you get

Concrete pieces, each one something you can open and check when it's done.

EmailsFieldsDocumentsAnswersRecordsReview queueLLM featurebuilt by Pushbuild

Extraction and classification

Emails, tickets or documents turned into fields and categories.

Structured outputs, validation

Document Q&A

Answers from your files and records, with citations.

Embeddings, pgvector, retrieval

Drafting assistants

Replies, summaries and reports drafted for staff to approve.

Prompts, templates, review queue

Evaluation harness

A test set and scoring so changes are measured, not guessed.

Test sets, scoring scripts

Cost and usage controls

Budgets, caching and logs per feature.

Middleware, dashboards

Typical projects

Example packages to show the shape of the work. These are examples, not past clients; yours gets its own written scope.

Example package

Feasibility test

One task tried on a sample of your real data, with a measured pass rate.

You get

  • Task and success measure agreed
  • Test set from your examples
  • Results and cost per item
  • Recommendation to build or not

Example package

Feature build

One AI feature built into your app or workflow with controls.

You get

  • Feature agreed in writing
  • Validation and review path
  • Cost limits and logging
  • Evaluation report

Example package

Document Q&A

Answers from your documents, with sources.

You get

  • Document set agreed
  • Indexing and refresh
  • Cited answers
  • Accuracy check on sample questions

How we approach it

The habits that keep this kind of project from going wrong.

1

Measure before building

We test on your real examples first, so you decide with a pass rate in hand.

2

Least data necessary

Only the fields a task needs are sent to the model, and sensitive ones are redacted or kept out.

3

Your keys, your bill

API keys and usage live in your accounts, with budgets set.

How we work

You describe it. We write it down, build it, and hand it over.

  1. You describe it

    Tell us what you need in your own words, and which tools you already use. A few lines is plenty to start.

  2. A written scope

    We reply with a short written scope: what gets built, what is out of scope, which accounts we need, and how we'll both know it works. You know what you're getting before work begins.

  3. Milestones you review

    The work is split into milestones. At each one you see it working in your own account and sign it off before we go on.

  4. Handover and a 30-day fix window

    You get the working system, its documentation and a walkthrough. If anything we built breaks in the next 30 days, we fix it at no charge.

What shapes the scope

  • Which task and how much accuracy it needs
  • Volume of items and documents
  • Where data lives and its sensitivity
  • Model and hosting choice

Always included

  • A written scope before work starts
  • Milestones you review and sign off
  • Documentation and a walkthrough at handover
  • A 30-day window for fixes, at no charge

Pushbuild has built software for businesses since 2010, for more than 40 clients. Every project is led by a former public-company CTO, from Groton, Massachusetts. See products we've built.

Questions people ask first

OpenAI, Claude or an open model?

It depends on the task, data sensitivity and cost. We test more than one on your examples when the choice isn't obvious.

Is our data used to train the model?

Business API traffic from OpenAI and Anthropic is not used for training by default under their current terms; we confirm the settings for your account in the scope.

What if the AI gets it wrong?

Every feature has a review path for low-confidence results, and the evaluation report shows the error rate before launch.

What happens after handover?

For 30 days we fix anything we built that doesn't work as scoped, at no charge.

Tell us what you need built.

Email a few lines about what you want and which tools you use, or call. We reply with questions or a written scope.

hello@pushbuild.com

(617) 299-8887