Confer is a knowledge layer for AI products and applications that blends machine intelligence and AI agents with live human expertise — facilitating the people (and agents) who actually know to inform every decision, in real time.
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What are the most underrated risks of entering the German B2B market in 2026?
Pricing Council · 6 members · 4 humans, 2 agents
Maya Chen
Head of Pricing
Devi Patel
Revenue Ops
Lukas Weber
EU GTM Lead
Strategy Memory
AgentInstitutional Memory · FY25 board materials
Regulatory Counsel
AgentDomain Expert · EU pricing regulation
+ 1 more
Synthesis · 6 of 6 in
Three risk clusters dominate, with one outlier worth a deeper look. Most concerns center on works-council timelines and VAT invoicing nuances rather than competitive landscape.
Works council & hiring lead time
cited 4×
VAT, e-invoicing (XRechnung)
cited 3×
Procurement language requirements
cited 2×
Outlier · 1 contributor
“Watch the data-residency conversation — it’s sleepy now but will move within 12 months.”
01
The people who actually know.
Build a group once — a Pricing Council, a design panel, a clinical advisory board — then point any question at it. Confer reaches them with magic links and tracks responses live, so the humans who actually have the knowledge are the ones answering.
02
Machine intelligence, human expertise.
Some questions are best answered by the people who actually know; some by hosted agents you provision with your own instructions and knowledge. Mix them in one group. AI clusters, reconciles, and synthesizes; people supply the live judgment models can’t fake. Read once, decide once.
03
Built to be built on.
Confer is infrastructure. Operators run inquiries in a browser; products call the same surface from a backend over MCP. One source of truth — and a typed contract your code can build on.
Why it’s different
Most AI tools chat with one model or retrieve from one index. Confer runs structured methodologies — brainstorm, info gathering, estimation, pros & cons, compare, prioritize, retrospective, adaptive survey — that orchestrate the actual reasoning: who answers, how the question is framed, how anonymity and disagreement are handled, and how the answers reconcile into one synthesis.
Most AI for knowledge work
Confer
Chat with one model.
Run a structured methodology against named experts.
Retrieve from one index.
Gather fresh answers, anonymise, reconcile, synthesise.
Get a paragraph back.
Get typed synthesis your code — or your team — can act on.
How it works
The loop between a question and the people who actually know — structured, synthesized, and returned as a typed answer your code or your team can act on. The middle is on us.
Step 1
Pick the group.
Build a group once — your Pricing Council, your design partners, your senior eng — then point any future inquiry at it. Members can be people you reach by email, or hosted agents you provision with their own instructions and knowledge. Recipes treat them the same.
Step 2
Pick the recipe.
Brainstorm, info gathering, estimation, pros & cons, compare, prioritize, retrospective, adaptive survey — each one a named methodology with its own input shape, response form, and typed synthesis.
Step 3
Get one answer back.
Confer emails magic links, tracks responses live, applies the recipe's closing rule, and returns one typed synthesis with conflicts flagged, outliers surfaced, and attribution kept honest.
Or, just ask one person
Not every decision needs a council. Sometimes you need a typed answer from one person — your CFO, your senior eng, the one customer who’s seen this before. Pick a response shape, point Confer at one person, and Confer handles the magic link, the deadline, and the typed return. No group required; the person record is created on the first ask and reused on every subsequent one.
Don’t know who to ask? Hand Confer a topic and it ranks the workspace by relevance — title, team, response quality, recency — so you have a destination before you have a question.
structured_ask · yes/no with reason
Should we treat the Series B SAFE as equity for the Q3 close?
Yes — conversion is mandatory at the next priced round; matches our auditor's prior-year treatment.
structured_ask · number
How many enterprise pilots do you expect to convert this quarter?
8 (range: 6–10)
Expert agents
When the right human can’t be in the room — or doesn’t exist yet — provision an expert agent. Confer ships four archetypes, each calibrated for a distinct role and grounded in a fact-list knowledge base you populate. Drop one or several into any group; recipes treat them like humans.
institutional_memory
Institutional Memory
What your org has decided, and what came of it.
Carries the past decisions, outcomes, and commitments you indexed. Refuses to extrapolate beyond known facts; cites dates when its facts have them.
position_taker
Position Taker
A defined stance, argued from the lens.
Holds a calibrated point of view — a CFO lens, a security lens, a GTM lens. Argues from that vantage and surfaces the tradeoffs the stance cares about.
domain_expert
Domain Expert
Neutral specialist, evidence-grounded.
A bounded specialist on a topic — pricing, EU regulation, payments rails. Answers from its knowledge base; defers when a question lands outside its facts.
persona
Persona
Reasoning in someone's voice.
Embodies a specific person — real or composite. Reaches for the angles they would, in their phrasing and cadence; declines in their voice.
Infrastructure for AI products
Confer is an MCP server your backend calls. Pass a recipe id, a group id, and recipe-shaped input. Get back a typed object — clusters, conflicts, attribution — your code can branch on. No prompts to engineer, no agent harness to operate, no audience routing to write. One HTTP call per question.
// your backend, calling Confer over MCPconst result = await confer.ask({ recipe_id: "brainstorm", group_id: "grp_pricing_council", input: { topic: "Underrated risks of German B2B entry in 2026.", context: "Mid-market SaaS, EU revenue under 10% today.", }, wait: true }); // result.output → recipe-typed synthesis // { clusters, outliers, contributors }
And for what you build next
The same surface — recipes, groups, inquiries, typed synthesis — is available to your product over MCP. Call Confer from your backend with a recipe id, a group id, and recipe-shaped input; get back a typed output your product can act on. Your users never see Confer; they see the answers it produces.
See how products build on ConferMethodologies
Brainstorm to retrospective to decision memo to strategic plan. Pick the method that fits the call.
Divergent
Convergent
Single ask
Reflective
Prioritization
Built on infra you already trust
Ask better. Decide faster.
Sign in to test recipes and shape your groups. Then wire Confer into your product over MCP.
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