Customer Acquisition Checklist for AI & Machine Learning

Prioritize segments where AI creates measurable leverage, like customer support summarization in fintech or semantic search in legal, and validate requirements for PII and PHI handling. Interview 10 target users to map current tools, pain points, and decision criteria.

Compare model accuracy and latency against current manual or rule-based processes, then translate to savings per seat or per ticket. Include GPU costs for A100 or H100, storage for embeddings, and tokens per request to produce a transparent cost-per-outcome number.

Document why buyers should choose you over OpenAI or Anthropic APIs and self-hosted Llama variants, focusing on differentiation like private deployments, data residency, or domain-tuned eval performance. Publish a concise matrix showing latency, cost, and accuracy tradeoffs.

Assemble a labeled test set that mirrors buyer data and edge cases, and score with metrics like groundedness, factuality, and retrieval recall. Use pairwise human preference judgments to calibrate prompts and models when automated metrics are insufficient.

Track time-to-first-successful inference under 5 minutes, p95 latency, and evaluation pass rate on your golden set. Set weekly SLOs and gate feature releases on meeting them to protect developer trust.

Identify SOC 2, GDPR, HIPAA, or FedRAMP requirements that affect logging, retention, and region selection. Offer EU-only processing and a data processing addendum so security reviews do not stall procurement.

Expose every endpoint with parameters, error codes, and example payloads so developers can test in 60 seconds. Keep examples realistic with streaming responses and JSON schemas for function calls or tools.

Publish idiomatic clients on pip and npm with async support, token counting, backoff policies, and middleware hooks for tracing. Include examples for serverless and containerized deployments.

Publish Colab and Jupyter notebooks that ingest documents, create embeddings with a vector DB like Pinecone or Weaviate, run retrieval, and evaluate groundedness with Ragas. Include a fine-tuning example that compares base, LoRA, and instruction-tuned variants.

Let users test prompts, upload small corpora, and toggle models like Llama 3, Mixtral, or GPT-4 while seeing tokens used, p95 latency, and estimated cost per run. Add shareable links so teams can align on prompt variants.

Support org-level keys, per-environment scopes, and temporary keys for client-side demos, plus IP allowlists. Enforce per-minute and daily quotas to prevent abuse while enabling trials to succeed.

Provide copy-paste snippets for common tasks, troubleshooting guides for token limits and context windows, and architecture references for RAG topologies. Add a migration guide for model swaps and versioning changes.

Run your golden set in CI using MLflow, Weights & Biases, or a custom harness and fail builds when regression thresholds are hit. Track prompt and model versions so you can reproduce customer results.

Measure retriever recall@k, answer faithfulness, and context utilization using tools like Ragas or custom scorers. Alert when chunking, indexing, or embedding changes degrade grounding.

Implement input and output filtering with Guardrails.ai or NVIDIA NeMo Guardrails and add jailbreak detection. Provide customer-tunable policies for toxic content, secrets, and data exfiltration.

Capture spans for each LLM call, retrieval hop, and tool invocation using OpenTelemetry, Langfuse, Arize, or WhyLabs. Normalize errors like rate limit, context overflow, and timeouts to speed up support.

Route traffic based on health and cost to alternatives like Claude, GPT, or local Llama, and canary new prompts or models to 5 percent of traffic first. Use feature flags to roll back instantly on degradations.

Use int8 or 4-bit quantization, KV cache reuse, batched generation, and TensorRT or ONNX Runtime with Triton Inference Server. Autoscale GPU nodes with Kubernetes and spot instances while keeping p95 under target.

Publish a detailed model card with license, eval numbers, and limitations, plus a Space that runs your API-backed demo. Add badges for tasks and link to docs and notebooks to capture developer interest.

Contribute connectors upstream so developers can drop your client into existing RAG pipelines with a single import. Provide examples for retrieval, tools, and structured outputs to lower switching costs.

Host monthly office hours, run Discord forums with tagged support channels, and sponsor small bounties for tutorial PRs. Launch a Kaggle-style challenge that uses your API and showcases winning solutions.

Publish comparison pages like RAG with Pinecone vs Milvus, or Claude vs GPT for legal summarization, backed by replicable notebooks. Target long-tail queries like 'best LLM for call summarization with PII' with real eval results.

List on AWS, Azure, and GCP marketplaces to streamline procurement and enable EDP drawdown. Offer metered SaaS contracts and private pricing tiers that map to enterprise budgets.

Create reference architectures with Pinecone, Weaviate, or Milvus and co-market with system integrators who own enterprise relationships. Provide MDF and joint webinars to accelerate pipeline.

Expose per-1k-token, per-request, and vector storage pricing with examples of monthly spend at typical workloads. Include spend caps, alerts, and cost forecasts based on recent usage.

Offer credits without a credit card, enforce rate limits, and ship a sample dataset so first value is instant. Trigger in-product nudges to upgrade when eval pass rates or traffic cross thresholds.

Ship SSO with SAML and SCIM, audit logs, private networking with VPC peering or PrivateLink, and optional on-prem or VPC deployments. Provide region pinning and data retention controls.

Publish uptime and p95 latency SLOs, a public status page with component-level metrics, and a root cause template for incidents. Offer credits tied to SLA breaches to build trust.

Prepare SOC 2 Type II, pen test summaries, DPA templates, and architecture diagrams covering encryption, key management, and data flows. Automate security questionnaire responses with a knowledge base.

Provide case studies showing accuracy gains, cost reductions, and time-to-value with replicable notebooks. Include a procurement checklist, RFP responses, and a one-pager for economic buyers.