Your best thinking.
Built into the operation.
We build tools for guest selection, research, writing, production briefs and performance reviews. They use your source material and channel history, with editorial decisions assigned to the people doing the work.
Put the channel’s history
to work in the next brief.
- The channel’s memory
We connect each published video to its topic, format, guest, script, packaging and results. Your team can ask what worked, for whom and under which conditions—and bring the answer into the next assignment.
- Strategy & prediction
We define editorial dimensions that matter for your formats and use available performance data to assess topics, guests, hooks and packaging. Forecasts make the assumptions explicit; a strategist makes the creative decision.
- Research to production
Research becomes a script, questionnaire or recording brief. The content’s central idea stays connected through trailers, Shorts and packaging, with source material and editorial direction available at each stage.
- Workflows with ownership
We map the actual handoffs, recurring decisions and bottlenecks. Then we build the automation, review points and working tools that let the team move faster while keeping responsibility clear.
- A learning operation
After release, the system adds the results to the production record. The team can compare the forecast with what happened and use that history when choosing the next guest, subject or package.
Start with a task
the team repeats.
Show us a recurring task, the material it uses and the result your team needs. We map the steps, choose what to automate and test the output against real examples before expanding the system.
Rishwajeet works with the people who use the system to define the editorial criteria and review points. The proposal sets out integrations, testing, handoff and any ongoing support.
The written proposal sets out the team, deliverables, responsibilities and investment. See how the services fit together.
For a worked example, see how to compare manual work, fixed automation and agents.
Where a system
can save repeated work.
The team repeats too much work. Research, briefs, publishing tasks and reporting often consume time without carrying useful context forward. We look for the parts that can become reliable systems.
You need stronger editorial decisions at scale. Guest selection, topic planning and packaging can benefit from explicit criteria, relevant history and well-placed human review.
You want the system and the content operation together. We can combine this work with content production or lead strategy with your existing team.
Systems built
inside working shows.
Money Matters: thousands of applications, a usable shortlist. Rishwajeet built the AI guest-selection system in four days. It reduced weekly review from days to under an hour, with the team making the final editorial call.
Masoom: an editorial model the production can use. Twelve dimensions connect guest and topic choices to actual performance. The finding reaches the research, questionnaire, trailer and package.
Scaler: connect the script to the released result. We track the releases we worked on, read their performance alongside their premises and packaging, and use the review to choose the next slate.
Look closer at the work.
Guest shortlists, source research, interview questions and a brief for the host. We read the guest’s previous work to find specific experiences worth discussing and questions they have not already answered.
Explore ↗Content performance predictionChoose what to forecast, record it before production and compare it with the release. Masoom’s guest-selection example shows why a forecast needs a specific audience goal.
Explore ↗Questions about
working together.
What is an AI content system?
It is a connected set of knowledge, tools and workflows built around a content operation. It can support audience research, strategy, guest selection, writing, production briefs, packaging and performance analysis, with human editorial judgment at the relevant decisions.
Do we need a large amount of historical data?
We first inspect the material and performance data available. Established channels can support richer comparisons and prediction models. Newer operations begin with audience research, explicit editorial criteria and a system that learns as more work is published.
Does the system make the creative decisions?
Rishwajeet and the editorial team make the creative decisions. The system provides research, context, forecasts and production support, then carries the results of those decisions into later work.
Can you improve an existing workflow?
Yes. We review how your team currently works, identify the recurring bottleneck, and build around the tools and responsibilities that make sense for the operation. The first proposal defines the workflow, integrations, review points and handoff.
Tell us what you’re trying to build.
Tell us about the channel or idea, the team you have and where you need help. We’ll discuss the scope on a 30-minute call.
Book a 30-minute call rishwajeet@machinehouse.media