Website Development · Data and analytics
Pairenthesis
Pairenthesis cleans and profiles messy data, answers plain-language questions with figures computed in code, and forecasts outcomes, with every step written to an audit ledger. No code required.
AI & Automation
AI features and workflow automation built on your data and shipped to production.
AI and automation covers two related jobs. The first is adding intelligence to your product: answering questions over your documents, summarizing records, classifying support requests, extracting data from forms and invoices, or generating first drafts your team reviews. The second is removing manual work from your operations by connecting the systems you already use and letting software handle the repetitive steps.
You need this service when people in your business spend hours each week copying data between tools, reading documents to find a few fields, or answering the same questions over and over. You also need it when a competitor has shipped an AI feature and your customers are starting to expect one.
We have built this kind of software for our own products, including the plain-language question answering in Pairenthesis and the scoring model behind Trading Town. We start from the business problem, measure whether the model is actually right, and ship features with the guardrails, logging, and fallbacks that production requires.
What's Included
LLM-powered product features
Question answering, summarization, drafting, and classification features built on models from OpenAI, Anthropic, or open-weight alternatives, with prompt design and evaluation handled by us.
Retrieval over your own data
Search and answer systems grounded in your documents, databases, and knowledge bases, so responses cite real sources instead of guessing.
Intelligent document processing
Extraction of structured data from invoices, forms, contracts, and reports, with confidence scores and a review step for anything uncertain.
Workflow and back-office automation
Automations that connect your CRM, email, spreadsheets, and internal tools, replacing copy-and-paste work with reliable, monitored pipelines.
AI assistants and chatbots
Customer-facing and internal assistants that handle routine requests, escalate to a person when needed, and log every interaction.
Evaluation, guardrails, and monitoring
Test sets, output validation, cost controls, and dashboards so you know how the system performs after launch, not just at the demo.
How a AI & Automation Engagement Works
- 01
Use case selection
We map the manual work or product gap, estimate the value of automating it, and pick the one or two cases with the best return.
- 02
Prototype and evaluate
A working prototype on your real data within weeks, measured against a test set so we know the accuracy before you commit further.
- 03
Production build
The prototype becomes a reliable feature or pipeline with guardrails, human review where needed, logging, and cost limits.
- 04
Monitor and extend
We track quality and spend in production, tune prompts and models as they change, and expand to the next use case.
Who This Is For
- Operations teams buried in manual data entry and document handling
- SaaS founders adding an AI feature customers are asking for
- Support teams answering the same questions across email and chat
- Finance and admin teams processing invoices, forms, and contracts
- Businesses with years of documents nobody can search properly
Tools and Technologies
- OpenAI API
- Anthropic Claude
- Python
- LangChain
- PostgreSQL with pgvector
- Node.js
- Next.js
- AWS Lambda
- Docker
- n8n
Related Work
Data Science · Financial services
Trading Town
Trading Town turns SEC Form 4 insider filings and congressional trading disclosures into ranked, searchable data with interactive charts, daily reports, and an API for investors who want evidence behind each trade.
Website Development · Marketing and lead management
SpamBurner
SpamBurner filters spam out of website contact form submissions and brings every lead, good or flagged, into one dashboard with custom filters, notification rules, and CRM integration.
AI & Automation FAQ
- How much does an AI or automation project cost?
- A scoped prototype on your data is usually a fixed-price engagement measured in weeks, so you learn whether the approach works before committing to a full build. Production builds are quoted after the prototype, either fixed-price or time-and-materials. Ongoing model and API costs are estimated up front and capped in the system.
- Will the AI make things up?
- Language models can produce confident wrong answers, which is why we ground features in your data, validate outputs, and add a review step wherever a mistake would be costly. We measure accuracy against a test set before launch and keep measuring in production, and we tell you plainly when a use case is not reliable enough to automate.
- Which models and providers do you use?
- We work with OpenAI, Anthropic, and open-weight models, and we pick based on your accuracy needs, data sensitivity, and budget rather than preference. Where data cannot leave your environment, we can deploy models privately. The architecture keeps the provider swappable so you are not locked in.
- How does an engagement work?
- We start with a short discovery session to choose the use case and define what success looks like. A prototype follows, built on your real data and evaluated together. If the numbers hold up, we move to a production build with monitoring and handover, then stay on for tuning and the next use case if you want us to.
- Do we need a data science team to maintain it?
- No. We build systems that your existing developers or operations staff can run, with dashboards for quality and cost, clear runbooks, and alerts when something drifts. Many clients keep us on a light support plan for model updates and prompt tuning, but nothing we ship depends on a specialist being on staff.
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Need AI & Automation?
Send us a short description of what you are trying to achieve. We reply within 24 hours with questions, a rough estimate, or both.
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