Technology Expertise
Python Development & AI Integration
We write the Python behind AI and data features: the pipelines that turn documents into clean records, the harnesses that measure whether a model is right, and the services your product calls. We write it for the engineers who will read it next year; how it is structured and tested depends on the engagement.
Start a Python project →What Python expertise does Code and Trust have?
We use Python for the backend of AI and data work when it fits: ingesting and cleaning documents, running evaluation sets against models, and serving results through APIs and background workers. The stack depends on the client and the scope, and we work in the languages and databases your team already runs.
Where Python fits
The language is chosen per layer and per client. Python often runs where its data and AI libraries matter most, and a web front end, often in another language, meets it at a typed API. Python is also a natural place for an n8n workflow to hand off when a step needs real processing or real tests.
- →AI and data backends: ingestion, enrichment, model calls and scoring
- →Background jobs and scheduled pipelines, including steps an automation workflow calls out to
- →Web front ends in whatever your team runs, often TypeScript, sharing a typed contract with the Python side
Document and data ingestion
Most AI projects succeed or fail before the model is called. Documents arrive as scanned PDFs, email threads and spreadsheets with a different layout every month, and the pipeline has to turn them into records the rest of the system can trust. Ingestion that holds up is re-runnable from the raw input, records which version of the parser produced each record, and puts anything it cannot read cleanly in front of a reviewer rather than guessing. How much of that a project needs depends on its documents and scope. Our document processing page covers the workflow side of the same problem.
- →Parsing and normalizing PDFs, emails and spreadsheets into typed records
- →Idempotent runs: reprocessing the same input gives the same result
- →Unreadable or ambiguous inputs flagged for review, not silently skipped
Evaluation harnesses
A model feature without a test set is a guess. We write the harness in Python: a fixed set of real examples with agreed correct answers, a scoring script, and a report that shows exactly which cases got better or worse when a prompt, a model or a retrieval setting changed. It runs before launch and again on every change, the same way the rest of the test suite does.
Services your product can call
Python work is exposed through an API service with request and response models defined in code (FastAPI is one common choice), so the interface is validated at runtime and documented from the same definitions. Long-running work, such as processing a batch of documents or calling a model many times, runs in background workers so the API stays responsive, and every job records its status so a failure can be found and retried.
- →Typed request and response models, in the framework the project calls for
- →Background workers for long jobs, with status tracking and retries
- →A database of record for job state and results, usually the one you already run
Written to be handed over
We write Python for the engineer who inherits it. Tests, type checking, pinned dependencies and a README that gets a new engineer from clone to running tests are set up to the level the engagement calls for. At handoff we walk your team through the code, and the source is yours.
Frameworks only when a project needs them
Whether to use a framework depends on the project. A thin layer of our own code over the model provider's SDK is often easier to debug and to change than a framework's abstractions; when a project needs more, the choice is made and explained in the scope.
Why Code and Trust uses Python
Code and Trust selects Python for projects where it provides a demonstrable engineering advantage: better performance, lower operational overhead, or faster iteration cycles. Every technology choice is justified by requirements, not familiarity.
Production-proven
Our Python implementations run in production at enterprise scale, not greenfield experiments. We understand the failure modes.
AI-native integration
We combine Python with modern LLM APIs and AI tooling, not as an add-on, but as a core architectural decision from day one.
Handoff-ready
Every project ships with inline documentation, automated tests, and a knowledge transfer session, so your team can own and extend it independently.
Python projects: common questions
Why use Python for an AI backend?
Because most data, document-processing and machine learning libraries are written for Python first, so the backend of an AI feature is often shorter and better supported in it. The front end can stay in whatever your team runs, sharing a typed API contract with the Python side.
Do you use an AI framework or call the model directly?
It depends on the project. Calling the provider's SDK with a thin layer of our own code is often easier to test and debug; a framework comes in when it saves real work, and that choice is explained in the scope.
How do you keep a Python codebase maintainable after handoff?
Tests on the business logic, type checking and pinned dependencies set up to the level the engagement calls for, and documentation a new engineer can follow. We walk your team through the code at handoff, and the source is yours.
Can you work in our existing Python codebase?
Yes. We start by reading it and running its tests, then agree on what to change and what to leave alone. If the codebase needs to be taken over from another vendor first, our software project takeover service covers that.
What does a Python project cost?
It is a fixed-price quote, and builds start from $15,000, scoped after the 90-minute AI workflow audit, which is complimentary when it is bundled with a larger build and available as a paid one-off on its own. Quick AI implementations take 2 to 4 weeks; larger ones take longer, up to months, depending on complexity.
Start a Python project with Code and Trust
Tell us what you're building. We'll scope it, resource it, and give you a fixed-price quote, with no billing-by-the-hour surprises.
Start a Python project →