Fine-tuning doesn't add knowledge, it adjusts behavior. Most prospects asking for fine-tuning actually need their model to know specific information, which is RAG's job. The bot explains this distinction and recommends fine-tuning only when the prospect needs specific output format or tone that prompting can't reliably achieve. It's a POV your senior consultants would defend in person.

Yes, agent workflows are scoped by tool count, conditional logic depth, and failure-handling complexity. The bot asks each and quotes the agent build tier with a complexity multiplier. It also flags the common pitfalls (tool misuse, infinite loops, unclear escalation, error opacity) that need design attention so the prospect understands what they're paying for.

Yes, the bot recommends by constraint. Latency-sensitive workloads point to smaller models (Haiku, GPT-4.1-mini, Gemini Flash). Output quality needs point to flagship models (Claude Opus 4, GPT-4.1, Gemini Pro). Cost-sensitive at high volume points to open-weights via Groq or Together. The bot's instruction codifies your firm's preferred routing per constraint and is updated as models evolve.

Some use cases are solved by regex, deterministic rules, or existing software features. The bot says so and either refers the prospect to a partner who can build the deterministic solution, or politely sends them home. This honesty preserves trust and ensures your booked calls are with prospects who actually need AI work.

Yes, the bot scopes a cost optimization audit at the audit tier price from postmeta. It captures current monthly inference spend, primary use cases, and current model choices. The audit then profiles calls, identifies routing opportunities, and quantifies savings. Typical results are 60-80% cost reduction on conversational workloads with no measurable quality loss.

Yes, the bot can recommend the right inference platform by constraint. Azure OpenAI for regulated enterprises with existing Azure footprint. AWS Bedrock for AWS-native shops needing model variety. Vertex AI for GCP customers wanting Gemini's long context. Together, Groq, and Fireworks for cost-optimized open-weights deployment. Your instruction codifies which platform you prefer per scenario.

The bot knows your consultancy's stance on self-hosted (Ollama, vLLM, TGI) and on-prem inference. Some prospects need on-prem for regulatory or data sovereignty reasons. The bot scopes those separately because they involve infrastructure work outside a typical RAG or agent project and price accordingly from postmeta.

On conversation end, SleekAI fires a webhook with transcript, scope classification, recommended model and platform, and contact info. Pipe that into HubSpot or Salesforce. Partners then see qualified prospects arrive with the scope already drafted and walk into discovery calls focused on architectural questions rather than basic disambiguation.