We build custom AI tools, MVPs, and internal systems for small teams. Fixed scope, fixed price, working software in weeks, not a six-month consulting engagement.
Systenics has been building software for 20 years. We now build AI tools, MVPs, and internal systems for founders and small teams who need working software, not a strategy deck. We also build and run our own products day to day, so "who maintains this after launch" isn't a hypothetical for us.
Every project is fixed scope and fixed price. You know what you're getting and what it costs before we start. Most builds ship in two to six weeks.
A working prototype is a weekend away. Software you can put customer data through and still trust in a year is a different thing to build, and a different thing to maintain.
Twenty years building software, most of it long before "AI" was the pitch. We know the difference between something that demos well and something that survives contact with real users, real data, and real scale.
csvnormalize.com, bankstatementscsv.com, and cardstatementcsv.com are ours: built, deployed, and maintained by us, every day. Most shops that build fast can't say that about anything.
A lot of what we've shipped (our own products and work we've done for clients) started as someone describing a problem, not handing us a spec. We've been doing this long enough to see the shape of a solution before the person in front of us does.
Cheap and fast gets you a demo. This gets you something you can still trust in eighteen months.
Four things we build most often. All fixed scope, all fixed price.
Get a working product in front of users in weeks. We build the smallest thing that proves your idea, then iterate.
For founders validating an idea
Turn PDFs, invoices, and bank statements into structured data automatically, no manual retyping.
For teams manually copying data from documents
A line-of-business app or internal tool built around exactly how your team works, not a generic template you have to bend to fit.
For teams outgrowing spreadsheets and manual process
Chatbots over your own documents, connections to AI providers, and useful third-party AI services wired into what you already run.
For teams that want AI in their existing systems, not a new one
Most small teams don't struggle with price. They struggle to picture a use case. That's usually where we start too: not a spec, a conversation about what's slow or broken.
Pull structured data (line items, totals, dates, vendor details) out of invoices, receipts, and PDFs, straight into your spreadsheet or system of record.
A chatbot your team can ask questions, that answers from your own manuals, policies, and past tickets instead of guessing.
Turn raw spec sheets or technical data into consistent, ready-to-publish product or listing copy, for a catalog with hundreds of SKUs.
Read incoming email or support tickets, sort by topic or urgency, and route each one to the right person or queue automatically.
Take a raw spreadsheet export and turn it into a written summary a manager can actually read, on a schedule, without anyone copy-pasting numbers.
Monitor a set of sources (competitors, filings, news) and produce a short summary of what changed, so nobody has to check manually.
Not case studies: products we built, ship, and run ourselves, every day.
Turns messy, inconsistently formatted CSV data into a clean, standardized structure — automated column mapping and data cleanup instead of manual spreadsheet wrangling.
Converts PDF bank statements into accurate CSV files in seconds. Built for accountants and bookkeepers who were previously retyping transaction data by hand.
Extracts transaction data from PDF credit card statements into clean CSV output. Files are processed briefly and deleted afterward.
You see working software every week. Scope and price are fixed before we write code, so there's no surprise invoice at the end.
A short call about the actual problem, not a feature list. Most of what we build starts here: we've found the real fix more than once by asking what's slow or broken, not by taking a spec at face value. What gets built, what it costs, and when it ships comes out of that conversation, fixed from day one.
We build in short cycles and show you working software every week. No black box, no surprise invoice.
Deployed, documented, and handed over. You own the code.
Optional ongoing support if you want us to keep improving it.
Practical writing on document AI, RAG, and building tools that hold up in production.
A practical guide comparing Vector RAG and navigation based RAG for large PDFs, showing how a hierarchical PageIndex lets LLMs locate answers inside structured documentation without relying on vector databases or similarity search.
Explore MarkItDown, Docling, and Mistral Document AI performance when converting complex PDFs into structured Markdown. This comparison highlights each tool’s strengths and limitations, from basic text scraping to AI-powered table recognition.
Read ArticleHow we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
How we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
Read ArticleFixed scope, fixed price, first reply within one business day.
