ScoringFactory Labs

Build in house.
With engineers scored for your build.

Our engineers, scored against your exact use case, build your product end to end. You own the code, the docs and the runbook.

Talk to the team who'll build it See a live Labs build
Illustrative example
1. Capabilities

What we build.
Built end to end.

Agentic coding & dev tools

A repo-aware agent that drafts pull requests, tests and migrations.

Data & analytics agents

An ask-your-database agent that writes the query and the weekly report.

Workflow & ops automation

An ops copilot that triages tickets, updates systems and routes exceptions to a person.

Knowledge assistants (RAG)

A cited answer engine over your docs, Slack and wikis.

Voice agents

A phone agent that verifies callers and answers account questions.

Document processing

A pipeline that turns contracts, invoices and claims into structured, checked records.

GTM & research agents

Go-to-market research: an agent that scores accounts and drafts the outreach.

Evals & observability

Evals and tracing that check every AI release before it ships.

Or anything else you need built. If it is software with AI at its core, bring us the brief.

Most AI builds stall on the team, not the tech.

2. How it works

From brief to shipped,
in three steps.

  1. Tell us the build.

    What it has to do, who will use it and the systems it has to work with.

  2. We score the team.

    We rank engineers on what your use case needs and staff the best fit.

  3. We ship it with you.

    Our team builds it, you review every release, and the code, docs and runbook are handed over to you.

3. Why Labs

The right engineers,
chosen for this build.

01

Staffed by score, not by bench.

A voice agent needs different people than a data pipeline. We score engineers on the skills your build needs, tie every point to work they have shipped, and staff the best fit.

02

Yours at handover.

You get the code, the docs and a runbook your team can operate from, so the product keeps running without us.

03

We show our work.

The Launch Video Library is a live Labs build. Use it, then judge us by it.

See the live build
4. Example case study

How a Labs build runs,
week by week.

Illustrative example · client anonymized

A software platform with a busy online community

People talk about the brand on every channel, all day. We built its community and social team an engagement assistant inside the client's own platform: like an AI support agent, but for community and social.

The problem

Questions, complaints and praise arrived across social networks, forums, review sites and the product's own community. The team read what it could and replied in the order posts came in, so an annoyed customer with a large following waited behind a routine thank-you. Each reply was written from scratch, and the voice changed with whoever was on shift.

The team we scored and matched

  • Agent engineerShipped AI agents that draft replies in a brand's voice, with a person approving before anything is posted.
  • Data engineerBrought live posts from many channels into one place, fast enough to answer while a conversation is still going.
  • Platform integration engineerBuilt features inside an existing product: its accounts, permissions, data and release process.

What we built

  • Community monitorBrings everyone engaging with the brand, on every channel, into one stream, with each person's history attached.
  • Engagement triageFlags what needs a reply now, what can wait and what needs none, so the team answers the posts that matter first.
  • Reply draftsAn on-voice draft for every post that needs an answer. A person edits and sends it.

Timeline

  1. Week 1

    Scope and score

    Map the channels, the reply history and the platform. Score engineers against that brief and staff the team.

  2. Weeks 2–6

    Build

    Monitor, triage and reply drafts built inside the client's platform, with a release to review every week.

  3. Week 7

    Handover

    Code, docs and runbook handed to the client's team, who run it from then on.

Outcome Illustrative, not measured results

  • Every mention, on every channel, lands in one stream inside the client's platform.
  • The team answers urgent posts first, not in the order they arrived.
  • Replies start from an on-voice draft, not a blank box. A person still approves each one.
  • The client's team runs and tunes it from the runbook, without us.
Start a Labs build

Bring us the brief.

Tell us what you want built. We'll reply with the engineers we would put on it, and why each one scored where they did.

Talk to the team who'll build it