AI Model Configuration

Configure which AI models AutoReach uses for different tasks. Each category has a primary model and a fallback model.

API Keys

Connect your AI providers by adding API keys in Settings > AI & Models:

• AI provider API keys- required for all AI-powered features (Anthropic and/or OpenAI)

• Email discovery API key- required for email finding (Findymail)

Model Categories

AutoReach uses AI across multiple categories. Each has independent primary and fallback model settings:

Category	Purpose
Content Writing	DMs, replies, first messages, templates, warmup, copilot
Buyer Scoring	Fit/intent/timing analysis and lead qualification
Keyword Generation	X + LinkedIn search keyword generation
Classification	Objection detection, negative content, deal signals, ICP matching
Profile Finding	Email, LinkedIn, X profile, and website discovery
Web Enrichment	Company research, news, social profiles, insights
Lookalike Search	Finding influencers similar to target profiles
Research & Copilot	Follow-up research, copilot web search, topic research
Lead Relevance	Post and tweet relevance analysis using reasoning
Call Briefs	Pre-call meeting preparation documents

Per-User Overrides

Each category can be customized with a primary and fallback model selection. The system validates your choices against available models and checks that the required API key is present. Invalid or missing configurations fall back to defaults.

How Fallbacks Work

If the primary model fails (timeout, rate limit, API error), AutoReach automatically switches to the fallback model for that operation. This provides resilience without manual intervention.

You do not need to do anything when a fallback occurs- the system handles it automatically and the output is still delivered. The only visible effect is that your cost reflects the blended rate of both models rather than just the primary.

If both the primary and fallback models fail (rare, but possible during provider outages), the operation is retried later.

When to Change Model Settings

Good reasons to change models:

• You want to reduce costs by using a cheaper model for high-volume categories like scoring or classification

• You want higher quality DMs or posts and are willing to pay more for a premium content writing model

• You have API keys for only one provider and need all categories to use that provider's models

When to leave defaults alone:

• If you are just getting started, the defaults are a solid starting point

• If your current output quality and costs are acceptable, there is no need to experiment

• If you are unsure which models to use, the defaults are well-tested

Cost Implications

Model pricing varies significantly. As a rough guide:

• Nano-class models are the cheapest option - ideal for scoring, classification, and keyword generation where volume is high

• Mid-tier models (Sonnet-class) offer a good balance and work well as fallback models

• Premium models (Opus-class) deliver the highest quality but cost considerably more per token - reserve these for content writing if budget is a concern

Since scoring and classification run on every lead while content writing only runs when sending messages, moving scoring to a cheaper model produces larger savings than optimizing content writing.

Recommended Configurations

Budget-conscious setup: Use nano models for scoring, classification, and keyword generation. Use a mid-tier model for content writing. This minimizes per-lead costs while keeping message quality reasonable.

Quality-focused setup: Use a premium model for content writing (DMs, posts, replies). Keep scoring and classification on nano or mid-tier models - these tasks do not benefit as much from premium models. This gives you the best message quality without overspending on scoring.

Settings UI

The AI Configuration section in Settings shows all categories with primary and fallback dropdowns. Changes take effect immediately and are validated on save.

Dynamic Cost Impact

Your model selections directly affect pipeline costs. The cost estimation system reads your actual model configuration and calculates blended pricing based on observed fallback rates. See Cost Estimation for details.

Next Steps

• Pipeline Cost Estimation: How model choices affect per-lead costs