Python Development

Python Development Services

Python is where your data pipelines and your AI integrations actually live — the layer that decides whether a dashboard can be trusted and whether a model's output is usable by anything downstream. Facet builds that layer: dbt-modelled transformations with tests on the data, orchestrated ingestion into your warehouse, and the Python services that ground LLM capability in your real business records. We build it the way we build it for ourselves, and that is not a figure of speech — the agent loops that run Facet's own engineering, content and quality review sit on exactly this Python layer, so the state management, retry logic, output validation and cost monitoring described further down this page are components our own week depends on working.

How could AI agents run your data and automation layer? Book a 45-minute working session on one real pipeline or process. You leave with a written recommendation.

The agent infrastructure on this page is infrastructure we run

Most firms selling LLM integration work right now are learning it on their clients. Facet is not, because for us this is not a practice area — it is the plumbing under our own operation.

That matters to you in three specific ways.

• We have already found where this breaks. A model that returns confident, well-formed, wrong output rather than failing. A context window that silently truncates the part that mattered. Retry logic that turns one bad call into a five-figure month. Every guardrail in the sections below exists because we hit the failure it prevents on our own systems first, where the cost landed on us.
• Cost monitoring is a first-class component, not a footnote. We instrument token and compute spend per run because we watch our own, and a loop with no cost signal is one bad prompt away from being the most expensive thing in your infrastructure.
• Humans stay in command, and agent-written code clears the same gate. Agents carry the volume — test generation, schema checks, boilerplate connectors. A Facet engineer owns the architecture and every merge, and a line an agent wrote passes exactly the review a line a person typed does.

Where Python earns its place

Three domains, in the order they usually matter.

Data engineering and the modern data stack

Facet's data practice is built on dbt and the open-source data stack. Python powers the ingestion, orchestration and transformation layers that feed everything your business reports on.

• dbt pipeline development: we model, test and document your transformations in dbt — lineage tracked, assertions running against your data contracts, incremental builds that hold as volume grows.
• Data warehouse builds: Python-orchestrated ingestion into Snowflake, BigQuery, Redshift or DuckDB — schema-stable, idempotent, and observable from the first run rather than after the first incident.
• Custom ELT pipelines: when an off-the-shelf connector does not cover your source, we build one, with the same test coverage and monitoring as everything else.
• Orchestration: Airflow, Prefect or a simpler scheduled runner, chosen to fit your actual scale — dependency graphs, retry logic and alerting designed in rather than bolted on.

AI and Agent Loop integrations

Python is the working language of LLM integration. This is the layer that turns a model's capability into something a business process can depend on.

• LLM-backed services: routing prompts, managing context, parsing and validating model output before anything downstream consumes it, with human checkpoints wherever your risk tolerance requires them.
• Agent Loop infrastructure: the harness that keeps a loop running — state management, tool integrations, retry and fallback behaviour, structured logging, and per-run cost monitoring.
• Vector search and RAG pipelines: embedding pipelines, vector store integration and retrieval APIs that connect your proprietary data to the model reasoning over it.
• Evaluation harnesses: automated checks on output quality that catch regressions before users do — the piece most teams skip, and the reason their pilot never reached production.

Automation and backend systems

• Workflow automation backends: ingestion, transformation, enrichment and dispatch, built to run unattended and to alert on exceptions rather than fail silently.
• API and third-party integrations: tested connectors to CRMs, marketing platforms, ERPs and proprietary sources, with retry logic, schema validation and pinned dependencies.
• Internal tooling: admin interfaces and ops dashboards on Python APIs — purpose-fit to your process, faster than bending a commercial product to it.
• Event-driven services: lightweight services responding to a new record, a completed transaction or a schedule, without a monolith underneath.

What the loop actually does

Once a pipeline is running, the recurring work is high volume, mechanically checkable, and almost always fine. That is the profile of work agents do well and people do badly.

• Run the data tests on every build. Row counts, null rates, referential integrity, distribution shifts, freshness against the agreed lag. Hundreds of assertions on every run, of which the interesting result is the one that fails.
• Watch the pipeline's own vital signs. Success rate, exception rate, runtime, cost per run, source-to-warehouse lag. A drift that shows up as a number in week one is a conversation; the same drift discovered in a board deck is an incident.
• Diff the upstream surface. Vendor APIs change schemas, deprecate fields and adjust rate limits on their own calendars. Matching those changes against what your pipeline expects is constant, tedious, and exactly where silent breakage originates.
• Generate the first pass of tests and connectors. Boilerplate against a known schema is checkable work with a right answer, which is why it goes first.

What a person still decides

• What the number means. A pipeline can prove a figure matches its source. Whether "revenue" in that source is the definition your CFO uses is a business decision, and it is where most reporting disputes actually live.
• What an anomaly is. A 40% volume drop is a broken feed, a seasonal pattern, or a real business event. The pipeline can only tell you it happened.
• Where the human checkpoint sits on an AI step. Which model outputs may act unreviewed and which must stop for a named person — written down before anything runs, owned by you.
• What to build and what to buy. Sometimes a $200-a-month connector covers it, and we will say so.
• Every merge, every production deploy, and every schema migration.

Show me the math

(Illustrative frame — replace every figure with your own. The arithmetic is the point, not the numbers.)

Take one reporting or data-prep process your team runs on a recurring basis. Count the hours it consumes in a normal month — not the ideal version, the real one including the re-runs after somebody finds a wrong number. Multiply by your own fully loaded cost per hour. That is the current annual price of the process.

Against it: a scoped pipeline is a build cost and a monthly operating cost, and both are quotable once the sources are known. Most of the decision turns on a single question — how often does that manual process produce a number somebody later has to correct? Teams who track that answer rarely need convincing; teams who have never measured it should, because it is the cheapest diagnostic available and it takes one month of honest note-keeping.

A pipeline nobody operates does not get more reliable. It stays exactly as reliable as the day it shipped while the world around it moves — schemas drift, volumes grow, an upstream vendor changes a field name. What improves a pipeline is the operating loop: tests added as new failure modes are discovered, alerts tuned as false positives are identified, transforms extended as the questions change. The mechanism is that list, not the elapsed time, and somebody has to be working through it.

How could AI agents run your data and automation layer? Book a 45-minute working session on one real pipeline. You leave with a written recommendation, including "buy the connector, do not build this" when that is the honest answer.

How an engagement runs

Scoping and architecture. We start at the bottleneck — the manual process, the fragile pipeline, the data question nobody can answer — and map what is actually happening before writing code, which reliably surfaces a different requirement than the stated one. We size for your real volume trajectory rather than a worst case. Open-source by default: maintained, community-backed libraries, no proprietary lock-in. Acceptance criteria are agreed in testable terms — throughput, latency, error rate, accuracy thresholds — before development starts.

Build, test, deploy. Unit, integration and data-contract tests written alongside the code. Every feature reviewed, every module documented, so the system is legible to somebody who was not in the room. Staging against production-equivalent volumes, then promotion with rollback and a runbook. Handover walks your team through the system and confirms monitoring is live before we step back.

Operate and improve. Production monitoring on error rate, latency, throughput and freshness, with alerts that fire before a downstream report is wrong. A scheduled review of what the metrics show and what to tune, extend or deprecate. An iteration backlog prioritized against business value rather than engineering preference.

Reported in operational terms: successful run rate, exception rate and mean time to recovery; source-to-warehouse lag and error rate against expected schema; cost per run, trended; and throughput against the manual baseline it replaced.

Where this goes next

• Agentic Engineering — the build layer, when the pipeline turns out to be one part of a larger software surface that should run the same way.
• AI Managed Services — the run layer. A named Facet team keeps the pipelines and integrations earning as your data, your sources and the models underneath them move.

Facet has been rooted in open-source ecosystems since 2014 and was named a CIOReview Top 10 Digital Transformation Services Company in 2021. A single pipeline delivered, running and needing nothing further is a complete outcome.

FAQ

What do custom python development services cost?

Engagements start at $6,000 for scoped project work and scale to retainers for teams needing continuous development and operation. The right entry point depends on what you are building — a standalone automation backend, a warehouse and pipeline layer, or an ongoing agentic development partnership. The first conversation is scoping, and it will tell you which shape fits before anything else is decided.

How is this different from hiring a Python freelancer or a dev shop?

A freelancer delivers code and a dev shop delivers a project; neither carries an operating mandate, so what happens at month six is your problem. Facet's engagements include the loop — monitoring, alerting, iteration, and accountability to the KPIs agreed before we started. The distinction is invisible at delivery and decisive a year later.

Do you work with our existing Python codebase?

Yes, and a significant share of engagements start that way. We begin with an audit: what it does, what tests exist, what its failure modes are, and what it would take to make it maintainable and observable. We will tell you honestly what we find, including when part of it should be replaced rather than extended.

How does Facet's data background apply here?

Facet has been in open-source ecosystems since its founding in 2014 — Drupal, Laravel, and increasingly the modern data stack: dbt, Airflow, Prefect and cloud-native warehouses. Our Python engineers are data and backend engineers who have run production pipelines and debugged dbt model failures, not generalists pivoting into data work.

Can we start with an AI integration rather than a pipeline?

Often that is the wrong order, and we will say so. An LLM integration grounded in data nobody trusts produces confident answers from bad inputs, which is worse than no integration. Where the data layer is sound, the AI layer is usually weeks rather than months.

Pick a number in your dashboard and trace it back

Here is what makes data work different from every other kind of software Facet builds: when an application breaks, it announces itself. When a pipeline breaks, the number simply changes, and a wrong number looks exactly like a right one. Nothing in the dashboard distinguishes them. Somebody acts on it anyway.

So the test that matters is not whether the code runs. Take any figure on a dashboard your leadership team uses and ask someone to trace it to its source — which table, which transform, which assumption about what counts. If nobody can do that in an afternoon, the problem is not that your reporting is slow. It is that your reporting is unverifiable, and every decision made on it has been made on faith.

That is what data contracts, lineage and tests on the data are for, and it is the part of this work with the clearest return: not a faster report, but a number somebody is willing to act on without checking it first.

How could AI agents run your data and automation layer? Book a 45-minute working session on one real pipeline or reporting process. You leave with a ranked list of the steps an Agent Loop could take over, the arithmetic run on your own volumes, and a written recommendation — including "your source system is the problem, fix that first and you will not need us" when that is what we find.

Book a working session →