pg_facets prepares the ground before they start
Born in MindFlight Orchestrator. Designed to work anywhere.
Modern agents shouldn't waste 70% of their time searching for the right information.
In every organization, there comes a moment when we realize that AI agents are no longer just executors. They become a system, an organism that takes the lead.
A support system that already knows what to look for.
Operations that prepare actions before they are triggered.
IoT flows that anticipate needs before signals.
Services that adjust as if they "guessed" what was coming.
This shift never happens by chance.
It appears as soon as a silent brick ensures that agents start each task with a head start.
pg_facets doesn't respond faster.
It prepares the ground before you even walk into the room.
With pg_facets, context is preloaded before the query
→ your agents become proactive, fast, and reliable.
Primary CTA: 🚀 Watch the 20-second demo
Secondary CTA: 📦 Access the PF-RAG Protocol (open-source)
Tertiary CTA: 🧠 Explore MindFlight Orchestrator
How MindFlight Orchestrator opened up a new paradigm ?
You think you understand, and then you realize there was a missing piece.
Teams that work with traditional agents all tell the same story:
everything works fine until the moment when the agent should already have known what they needed to know.
No drama.
No spectacular mistakes.
Just that slightly annoying feeling:
"He's still searching. He could have known."
And then you discover MFO and its logic — not by reading about it, but because its agents start behaving differently. You observe responses that seem to emerge from a context that is mysteriously already in place.
You then conclude the only reasonable explanation:
The problem was never AI.
The problem was everything it had to catch up on after the fact.
This is how, almost unintentionally, an idea is born:
anticipate → reason → deliver.
pg_facets is just a natural consequence of this.
And a portable consequence.
Inevitable memetics:
“Stop retrieving. Start foreseeing.”
The Promise : Give your agents the answer during the question
With pg_facets, your agents become capable of:
- ⚡ Responding instantly to complex queries thanks to intelligent preloading
- 🎯 Drastically reduce hallucinations
- 🧭 Detect future intentions and prepare the right sources
- 🔄 Standardize RAG instead of cobbling together patches
- 🚀 Scale your agent logic without exploding costs or latency
Core promise: Your agents go from reactive to predictive.
When the question comes too late.
What is surprising about pg_facets is not that it responds quickly.
It is that it seems to have already done the work.
Teams observe:
- answers that arise with disconcerting obviousness,
- mental detours that never happen,
- oversights that... no longer happen,
- agents who have "prepared something" before being asked.
No one needs to be told what this means.
They guess instantly: Work is no longer reactive. It becomes predictable.
Agents stop responding: they guess.
Why this idea could only have come from MFO ?
When you orchestrate agents capable of:
— chain reasoning,
— cooperating,
— maintaining a context that moves faster than a project manager on caffeine,
a recurring pattern emerges:
— agents rarely fail because they are stupid,
— but very often because they arrive "too late" in the conversation.
So one day, at MFO, someone said:
"What if they already knew?"
No one laughed.
Because it works.
pg_facets is simply the open-source version of this discovery.
No commitment, no conversion, no sacred temple.
But optimized for those who want to push MFO even further.
MindFlight Orchestrator was designed to orchestrate agents capable of:
- reasoning across multiple steps,
- cooperating,
- maintaining dynamic context,
- managing different level of memory,
- executing complex workflows in production.
We quickly realized this:
Agents fail not because of a lack of intelligence,
but because of a lack of anticipation.
Anticipation build context.
Agents love contextual prompting.
So MFO invented contextual pre-fetching.
pg_facets makes this innovation accessible without adopting MFO, while offering native integration for those who want it.
How it works (leaving your brain to do the rest)
1️⃣ Anticipate
Signals and usage patterns reveal a pattern.
You just need to look twice to see it.
2️⃣ Reason
The protocol decides what to load, condense, or keep in reserve.
You naturally deduce that the agent is building a launch pad.
3️⃣ Deliver
When the question comes, there's nothing left to fetch.
Everything was already warm, ready, orchestrated.
And then, a phenomenon appears in your logs:
complex answers appear faster than simple ones.
(Strange, but not that strange.)
Scenarios that were impossible before pg_facets:
- Instant response to a complex analysis, because the agent has anticipated the subject.
- Automatic preparation of 5 critical sources during the first question.
Who is it for?
✔️ You will recognize yourself if:
- You build agents that need to understand before responding.
- You are tired of micro-patches and pseudo-optimizations.
- Your team wants something clean, reproducible, and scalable.
✖️ You will not recognize yourself if:
- You want a chatbot that recites a PDF.
- You do not have access to your architecture.
- You do not want to anticipate anything.
Investment — The cost that no one quantifies, but everyone feels
When teams start measuring the time lost between:
"wait, I'm searching → wait, I'm compressing → wait, I'm restarting,"
they all end up reaching the same conclusion:
latency is not technical. It's structural.
Three options:
- Continue as before
- Build your own pre-fetch
- Use what already exists
At this point, the decision usually makes itself.
The real cost?
Every second lost in the "retrieval" zone slows down your business.
Before / After : The visible change
Before (Normal Agent)
- Reasoning: "search → search → hallucinate → apologize"
- Latency: ~4s
- Context: incomplete
- UX: slow, fragile
After (pg_facets powered Agent)
- Reasoning: "anticipate → prefetch → compress → answer instantly"
- Latency: ~300ms
- Context: targeted, ready
- UX: fast, proactive, reliable
Conclusion
Stop retrieving. Start foreseeing.
Upgrade your agents to predictive generation.
- 🚀 Watch the 20-second demo
- 📦 Access the PF-RAG Protocol
- 🧠 Explore MindFlight Orchestrator
