AI that does real work.
Not another disconnected chatbot. We build AI into the systems you already run: document processing, workflow automation, assistants that know your business, and product features your customers actually use. Designed, built, and operated by the team that runs your software.
- 1 Pick one workflowThe process everyone complains about, with a number attached.
- 2 Pilot it with a human in the loopAI drafts, your team approves. You find out fast and cheaply.
- 3 Measure, then widen or stopA before and an after. If it did not move, we say so.
If no process fits, that is a finding too.
Most AI projects fail the same way: a demo that impressed everyone and then never touched real work. We build the other kind. Practical AI connected to your actual data, your actual workflow, and a measurable outcome, with a human in the loop where it matters.
Where AI pays for itself.
Document processing and extraction
Invoices, contracts, applications, reports: read, classified, and turned into structured data your systems can use. The clerical work nobody was hired to do.
Workflow automation with approval
AI drafts, a human approves, the system files. Automation that speeds people up without taking their judgment out of the loop.
Internal knowledge assistants
An assistant that has actually read your SOPs, your product catalog, and your past tickets, and answers from them, not from the open internet.
AI product features
Recommendation, personalization, generation, and scoring features built into your existing product, shipped like any other production feature: staged, tested, monitored.
See how we build products →Search, classification and summarization
Find the record, route the request, summarize the thread. Small, boring wins that compound daily.
Content and research pipelines
Structured pipelines that draft, check, and publish with editorial control, instead of one-off prompting in a chat window.
Measured like software, because it is software.
Start with the workflow, not the model
We map where time actually goes and pick the one process where AI moves a number you care about. If no such process exists, we say so.
One process, not a platformHuman in the loop by design
Drafts get approved, extractions get spot-checked, and confidence thresholds decide what is automatic versus what is reviewed. Trust is earned in production, gradually.
Judgment stays with your teamOperated after launch
Prompts drift, models update, edge cases arrive. AI features get the same monitoring, versioning, and ownership as every other system we run.
Monitored, versioned, ownedAI we’ve already shipped.
An AI content operator we run daily
Conductor, Parameter's internal system, plans, drafts, and publishes content across six product sites on the Claude API, with editorial rules and human review built in. We built it because we needed it, and we operate it every day.
An AI-visibility scoring engine
Frontpagely, a Parameter product, measures how AI assistants see and cite a website and generates a prioritized fix plan. AI analyzing AI, productized.
AI product features for clients
A custom-blend recommendation experience built into a specialty e-commerce retailer's storefront, shipped and monitored like any other production feature.
Automation pipelines in production
Translation pipelines wired into a bilingual law firm's CMS, document generation, and internal tooling: automations that run unattended, with checkpoints where it matters.
What we won’t sell you.
An "AI strategy" deck with no software attached.
A chatbot bolted onto your homepage so you can say you have AI.
A replacement for judgment calls that belong with your team.
If the honest answer is that an inexpensive off-the-shelf tool covers it, that’s the recommendation you’ll get.
Straight answers.
Which models do you use?
Commercial frontier models, chosen per task rather than by loyalty. We are model-pragmatic: the best choice for pulling data out of documents is not always the best choice for drafting text. Wherever the provider supports it, your data stays in your own accounts.
What does a first project look like?
One workflow, a few weeks, and a measurable before and after. We pick a process where AI can move a number you already track, pilot it with a human approving the output, then compare. Small enough that finding out costs little.
Can you add AI to software you did not build?
Yes, and that is the common case. Most systems we add AI to were built by someone else. If the data lives somewhere we need to reach, that is integration work, and it is a practice of ours.
How do you handle our data?
Scoped access to only what the task needs, and we review the provider data-use settings with you before anything is wired up. Nothing is trained on your data without your explicit decision. We would rather describe what we actually do than point at a certificate.
What if it does not work?
You find out fast and cheaply, because that is how we design pilots. If the result does not justify the work, we tell you and we stop. Learning that AI does not belong in a process is a real outcome, not a failed project.
Do you build custom copilots?
Yes: assistants tied to your own data and processes, respecting the permissions your team already has. An assistant that can see everything is a liability, so access follows the same rules your systems already enforce.
Bring us the process everyone complains about.
Tell us about the reports someone assembles by hand every Friday, or the inbox that takes half a day to triage. We’ll tell you if AI belongs there, and what we’d build if it does.
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