Someone spends every Monday copying data into a spreadsheet.
What we doA script collects it, cleans it and drops it where it is needed, on a schedule, with a log that says what ran and what didn't.
Python, Scraping & Data Automation
The Monday spreadsheet job, done by a script.
Python scripts, scrapers and FastAPI back-ends that take repetitive data work off your team's plate: collecting, cleaning, checking and reporting, with logs you can read and safeguards that stop a script from doing something stupid at 3 a.m.
The problem
Busywork with a spreadsheet at the end.
Every business has a job like this. Someone opens five websites, copies prices or listings into a spreadsheet, fixes the formatting, removes the duplicates, and emails it round. Or a lead list arrives with half the emails invalid and the names in three different formats, and someone cleans it by hand before it can go near the CRM.
It is repetitive, it is error-prone, and it eats the hours of people who should be doing something else. It is also exactly what a well-built script is for.
The other version of the problem is bigger: a product that needs a Python back end, and it has to hold up when requests are retried, workers restart and outside services time out.
What we build
Scripts, pipelines and APIs.
- Scrapers and collectors. Public listings, product pages and directories gathered on a schedule, at a polite rate, into a clean structure.
- Data cleaning and enrichment. Deduplication, formatting, email verification and enrichment steps that run before data reaches your CRM or your team.
- Browser automation. Selenium for the jobs where there is no API and a person currently clicks through the same forms every day.
- File and media processing. Batch jobs such as image conversion and resizing, built to run unattended.
- CRM helpers. Small scripts that push clean data into a CRM through its API.
- FastAPI back-ends. Typed, tested services behind web apps and AI agents, including background jobs and the safeguards that make retries safe.
How we work
Scope, build, hand over.
1. Scope the job and the source. What goes in, what comes out, how often, and who reads the result. For scraping, we check the site's terms and look for an official API first.
2. Build it to fail loudly. A script that quietly returns bad data is worse than one that stops. We validate inputs and outputs, log every run, and alert when something looks wrong.
3. Make retries safe. Anything that writes data is built so running it twice doesn't create duplicates. In our FastAPI builds that means idempotent writes, unique keys and reconciliation before a retry.
4. Deploy where you can see it. A scheduled job on your server, a container, or a cloud host, with backups where it holds data and a short runbook.
5. Hand over. The code, the schedule, the log location, and a note on what to change when the source changes.
Proof
Python in the builds we ship.
Selenium and web scraping have been part of our founder's toolkit since his full-stack developer roles, and he keeps a library of automation scripts: lead and product scrapers, form bots, email verification and enrichment tools, image conversion, and a GoHighLevel integration helper. Client scripts aren't published; the builds below are public.
- ThesisCircuit (team build for the Alpaca AI Trading Agents hackathon, paper trading only). A Python and FastAPI back end with durable order intents, atomic claims and reconciliation by order ID before any retry. The final validation run recorded 256 backend tests passing.
- NeighborOps AI (team build for the Agents for Humans hackathon). FastAPI and SQLAlchemy behind a Strands agent, with idempotent reservations enforced by a conditional update and a unique allocation key.
- BrandPilot Local (team build for the AMD AI DevMaster hackathon). A FastAPI job service using Pillow and OpenCV for image intake, with an authenticated job endpoint for local GPU inference.
Stack
What we reach for.
Python throughout; Selenium for browser automation; FastAPI and SQLAlchemy for services; Pillow and OpenCV for images; automated test suites and Ruff for linting; Docker for packaging; and Railway, a VPS or your own server to run it. We schedule with cron or the host's own scheduler, and we pick parsing and data libraries per job rather than by habit.
Not a fit
When we'll pass.
- The data sits behind a login you aren't authorised to automate, or the site's terms forbid it. We don't bypass access controls.
- You want to harvest personal data for cold outreach without a lawful basis. We won't build that.
- The job runs once and takes an hour by hand. Do it by hand; a script isn't always the answer.
Problems we fix
If one of these sounds familiar, we should talk.
Our lead and contact lists are a mess.
What we doDeduplication, formatting and verification steps run before anything reaches your CRM, so bad records stop at the door.
We need a back end, and it has to be Python.
What we doFastAPI services with typed inputs, tests, and the safeguards that matter when a request is retried or a worker restarts.
What you get
A system, not a pile of parts.
Pick the pieces you need. Most projects start small and grow from there.
Scrapers that respect the source
Collection from public pages at a polite rate, with the site's terms checked first and an official API used whenever one exists.
Clean data pipelines and reports
Validation, deduplication, enrichment and formatting steps, then an export, a sheet or a report in the shape your team already uses.
FastAPI back-ends
Typed, tested APIs with authentication, background jobs and idempotent writes, deployed with Docker where it helps.
Proof
Related work and reading.
Client work is shown anonymised. Hackathon builds link to their public case studies.
ThesisCircuit (case study on talalkhawaja.com)
Paper-only options research agents: three strategies compete, a critic objects, and a fail-closed risk governor has the final say. NO TRADE is a first-class, audited result.
NeighborOps AI (case study on talalkhawaja.com)
An operations dashboard for community pantries: a Strands agent handles routine coordination through narrow tools, and people decide how scarce resources are used.
BrandPilot Local (case study on talalkhawaja.com)
A local, review-first creative studio prototype that turns product images and campaign briefs into reviewable marketing concepts, with real SDXL inference verified on AMD Radeon.
Stack
What we work with.
- Python
- FastAPI
- Web scraping (Selenium)
FAQ
Straight answers.
It depends on the site, the data and where you are. We check the site's terms and prefer official APIs. We won't scrape behind logins we aren't authorised to use, collect personal data without a lawful basis, or bypass access controls. If the job needs any of that, we will say no.
Scrapers eventually do, because the source changes. We build them to fail loudly rather than quietly return bad data, and we keep the selectors in one place so fixes are quick.
Wherever suits you: a scheduled job on your server, a small container, or a cloud host. You own the account and the code.
Yes, through the CRM's API, a webhook or an import file. We validate and deduplicate first, because pushing dirty data into a CRM is harder to undo than to prevent.
We don't publish fixed prices. A one-off script and a scheduled pipeline with monitoring are very different jobs, so we scope first and quote the actual work. Email info@teqprotech.com.
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