Sonar Pro (Perplexity): To build a platform like consensable.com (a “multi‑AI truth synthesis engine” that queries multiple models in parallel, compares them, and produces a synthesized answer), you need to design for three main layers: frontend UX, backend orchestration, and model/logic for synthesis.

Below is a detailed, practical procedure you can follow. Clarify the product and requirements

Define in one sentence what your app does, for example: “My app’s one job is to help users get a synthesized answer by querying multiple AI models at once.”[2]

Then define: Who uses it (researchers, journalists, general users). What they can do: Enter a question. See each model’s response. See consensus/disagreement analysis. See a final synthesized answer. Constraints: Which AI providers you’ll support (OpenAI, Anthropic, etc.). Cost per query and rate limits. Whether you require login, history, private results, etc.[4]

Validate the idea with potential users (interviews, landing page, etc.) before heavy development.[2] Choose architecture and tech stack

High-level architecture: Frontend web app (React, Vue, Svelte, or a no-code front-end tool). Backend API service that: Accepts the user query. Dispatches it to multiple AI APIs in parallel. Collects responses. Runs consensus/synthesis logic. Returns structured data to the frontend. Database: Users, sessions, queries, model responses, synthesized answers. Optional: background job queue for long or batch queries.

You can: Code it from scratch (maximum flexibility).[5][8] Use a no-code/low-code backend (e.g., tools that auto-generate web apps).[2][8] Mix: coded backend with a no-code frontend. Implement core backend flow

a) Endpoint design

Create an endpoint like: POST /api/ask Body: { question, selectedModels, options }

Backend steps: 1) Validate input (length, safety filters). 2) Create a “query” record in DB. 3) For each configured model: Build provider-specific prompt / parameters. Call the API asynchronously (parallel calls). 4) Wait for all responses or a timeout. 5) Store raw model responses. 6) Run consensus/synthesis algorithm. 7) Return: synthesizedAnswer perModelAnswers consensusAnalysis (agreements/disagreements, confidence) metadata (tokens used, latencies, etc.).

b) Parallel querying logic

Implement a model-agnostic interface, e.g.: A function for each provider: callOpenAI(), callAnthropic(), etc. A standard response schema: { modelName, text, citations?, error }

Use language/platform features for concurrency (async/await + Promise.all in JS, async tasks in Python, etc.) to query models in parallel, similar to the “querying models in parallel” behavior described by Consensable.[4]

c) Synthesis and consensus logic

Start simple, then iterate: Normalization: Clean text (strip formatting, normalize whitespace). Optionally split into sentences. Agreement detection (basic version): Ask another model to analyze: “Given these answers from multiple models, where do they agree, where do they disagree, and what is the best combined answer?” You send the list of model answers as context and get back a structured analysis (e.g. JSON with “pointsofagreement”, “pointsofdisagreement”, “final_answer”). More advanced options as you grow: Embedding-based similarity to cluster similar statements. Rule-based checks for factual conflicts (dates, numbers). Confidence scores based on how many models agree on a point. Frontend design and UX

Key screens: Question input page: Text box for question. Optional: model selection, temperature, max tokens. Submit triggers /api/ask. Results page: Synthesized answer at the top. Below it, a section for “Consensus & Disagreements”. Tabs or columns for each individual model answer, as Consensable hints: “analysing consensus & disagreements” and “synthesising final answer”.[4] Indicators for: Overall agreement level. Flags where models strongly disagree. Account / history (optional, but Consensable supports):[4] List of past questions. Ability to re-open and re-run with new models.

Focus on a minimal viable feature set first, rather than adding everything at once.[1][2] Data model and persistence

At minimum, design tables/collections for: Users: id, email, auth provider, plan. Queries: id, userId, question, createdAt, options, status. ModelResponses: id, queryId, modelName, rawResponse, tokensUsed, latency, error. Syntheses: id, queryId, synthesizedAnswer, consensusSummary, metadata.

You can use tools that simplify DB setup and auth together (e.g., platforms that integrate auth + database via a guided workflow).[2] Authentication, plans, and limits

Consensable offers more features after signup (unlimited questions, more models, private results, etc.).[4] You can mirror this by: Implementing: Public/anonymous mode with strict rate limits. Logged-in mode with: Higher limits. More models. Private history. Adding: API key management for your users if you eventually expose your own API. Role and plan fields (free, pro). Cost control, logging, and monitoring

Because each query fans out to multiple paid APIs, you must: Track: Token usage per provider. Latency and error rates. Set: Per-user daily limits. Global caps per provider. Implement: Caching of identical recent queries, if useful. Circuit breakers when providers fail or are slow. Privacy, safety, and legal basics Do not log highly sensitive content in plain text; consider encryption. Add content filters: Before sending prompts to models. Before showing outputs (to handle unsafe outputs). Provide clear terms and privacy policy (especially if you store queries and model outputs for analysis). Iterate on product and algorithms

After a basic version works: Run a small beta with a focused user group.[2] Collect feedback on:

Gemini 3.1 Pro (Google): Creating a platform like Consensable, which acts as a multi-AI truth synthesis engine, requires a well-planned architecture that can query multiple artificial intelligence models simultaneously, analyze their responses, and generate a unified answer. Consensable

GPT-5.4 (OpenAI): A platform like Consensable appears to work by sending one user query to multiple major models in parallel, having each model do real-time web search before answering, and then using a separate “chair” model to synthesize the panel’s outputs into one answer while exposing agreement and disagreement. It also supports modes such as general ask, voting, and ranking, and returns a panel-agreement score. (consensable.com)

A practical way to build something similar is to treat it as an orchestration system rather than a single chatbot. At a high level, your system needs seven layers: frontend, API gateway, job orchestration, provider adapters, retrieval/search, synthesis/evaluation, and storage/observability. Comparable products in this space consistently use parallel answers, visible disagreement, and a separate consensus layer instead of hiding raw model outputs. (consensable.com)

The detailed procedure is:

Step 1: Define the product contract. Decide exactly what the user sees. For example: one prompt box, model selection, mode selection, answer length, source visibility, consensus score, and expandable raw outputs. Consensable says it auto-detects mode or lets users force a mode with slash commands, and synthesis can take 15–60 seconds depending on depth. That implies your UX must support asynchronous jobs, progress updates, and partial rendering. (consensable.com)

Step 2: Choose an initial architecture. Use a web app frontend, a backend API, a queue, workers, and a database. Typical stack: Next.js frontend, Python FastAPI or Node backend, Redis or SQS queue, Postgres for persistence, and object storage for logs/artifacts. The queue is important because multi-model requests are slow, expensive, and failure-prone. You should create a job per user prompt, then sub-jobs per model.

Step 3: Create a canonical internal schema. Define one normalized request object with fields like prompt, mode, selectedmodels, retrievalenabled, maxtimeseconds, budgetcents, responselength, and usercontext. Also define a normalized response schema with answertext, citations, confidence, toolcallsused, tokenusage, latencyms, and errors. This is essential because each provider returns different formats. Products in this category emphasize direct parallel provider responses and then a structured consensus layer on top. (consens.io)

Step 4: Build provider adapters. Implement one adapter per provider: OpenAI, Anthropic, Google, Perplexity/Sonar, xAI, Mistral, DeepSeek, and others if needed. Each adapter should handle authentication, retries, rate limits, timeouts, model capability metadata, and output normalization. Keep adapters isolated behind one internal interface so you can swap models without changing business logic.

Step 5: Add retrieval and web search. Consensable’s help page says every panel member performs a real-time web search before answering. If you want similar behavior, either use providers with native search or build your own retrieval pipeline: query rewriting, web search API, page fetching, content extraction, ranking, and context packaging. Then pass the same evidence pack to each model, or let each model search independently if the provider supports it. The shared-evidence approach is cheaper and easier to audit; independent search may produce more diverse findings. (consensable.com)

Step 6: Run models in parallel. When a prompt arrives, fan out requests concurrently to all selected models. Enforce per-model timeout and global timeout. Save raw outputs immediately. If one model fails, continue with the rest. Consensus systems in this space are built around parallel answers first, not serial chains. (consens.io)

Step 7: Add answer parsing and claim extraction. Before synthesis, parse each model’s output into structured units: final answer, key claims, cited evidence, uncertainties, and recommendations. You can use either deterministic parsing rules or another model pass to extract claims. This enables agreement scoring and contradiction detection.

Step 8: Build the synthesis layer. Use a separate “chair” model to produce the final answer from the panel outputs. Consensable explicitly describes a chair model that synthesizes multiple commercial model responses into one accountable answer. Your synthesis prompt should instruct the chair to preserve minority disagreement, avoid inventing consensus, cite supporting evidence, and state uncertainty clearly. (consensable.com)

Step 9: Compute agreement and disagreement. Create simple first-pass metrics: semantic similarity between model answers, overlap of extracted claims, and contradiction flags. Then compute a panel-agreement score. Consensable surfaces agreement and disagreements explicitly, which is an important trust feature. (consensable.com)

Step 10: Support multiple modes. At minimum implement: Ask: synthesize one best answer. Compare: show side-by-side outputs. Vote: ask models to choose among options and justify. Rank: ask models to order options and explain criteria. This mirrors the modes described by Consensable and similar multi-model products. (consensable.com)

Step 11: Add cost and latency controls. This type of platform becomes expensive quickly. Use model tiers, token caps, caching, prompt compression, and staged execution. Example: first run 3 cheaper models; if disagreement is high, escalate to premium models. Other multi-model tools highlight cost analysis and timing because this is operationally critical. (concilium.dev)

Step 12: Build trust and governance features. Store raw provider outputs, source lists, model versions, timestamps, token usage, and synthesis prompts. Show users which models were queried and where disagreement occurred. Add abuse filters, PII controls, prompt-injection defenses for retrieved webpages, and per-user spend caps. Platforms in this category often emphasize privacy, direct provider routing, and transparent disagreement. (consens.io)

Step 13: Evaluate offline before launch. Create a benchmark set of prompts across coding, research, shopping, current events, factual

Grok 4.1 Fast (xAI): No reliable information found on the specific website consensable.com or its exact technical implementation. No reliable information found on the detailed procedure to create a platform exactly like it.

I am unable to provide a detailed procedure for building such a platform because specific details about consensable.com are not available from reliable sources. This is a technical topic involving software engineering, API integrations, and AI model orchestration, which requires expertise beyond general knowledge and needs individual project scoping based on requirements like cost, latency, and accuracy.