Customer Acquisition Checklist for AI & Machine Learning

Prioritize problems where you can access domain data and close the loop with user feedback. Without labeled or reliably annotatable data, iterative improvement stalls and customer outcomes suffer.

Define target precision/recall, calibration, or win rate and tie to metrics like reduced review time or increased conversion. Use domain evals rather than generic scores to avoid optimizing for irrelevant benchmarks.

Map prospects to SaaS, VPC, or on-prem based on privacy needs, residency, and inference latency. This controls your pricing, sales motion, and procurement path from day one.

Calculate total cost of ownership versus internal ML teams, including GPU hours, labeling, MLOps, and ongoing eval. Use this to anchor ROI messaging and objection handling.

Document HIPAA, GDPR, SOC 2, and audit requirements for your vertical. Govern data flows and retention so pilots are not blocked by security reviews.

List required APIs, webhooks, SDKs, and data connectors to embed in existing workflows. Highlight low switching friction in sales materials to ease adoption.

Create task-specific tests and regression checks for your product use cases. Avoid overfitting to public leaderboards by curating representative, anonymized customer examples.

Use Label Studio, Scale, or in-house SMEs to produce high-quality references with clear grading criteria. This anchors offline evaluation and aligns expectations with buyers.

Incorporate AdvBench, GANDALF, and prompt injection cases into CI. Block deploys on safety regressions to protect enterprise accounts from abuse.

Integrate a vector DB like Pinecone, Milvus, or pgvector. Track chunking, embedding model choice, and HNSW parameters to ensure retrieval quality and speed meet SLAs.

Use MLflow or Weights & Biases for versioned datasets, hyperparameters, and model lineage. Reproducible baselines speed procurement and security reviews.

Run Perspective API, Detoxify, or policy classifiers and log false positives. Present safety posture in sales collateral to reduce friction with compliance teams.

Evaluate vLLM, TensorRT-LLM, Triton, or ONNX Runtime and measure tokens per second per dollar on A10, A100, or H100. Select based on throughput, context length, and memory footprint.

Use AWQ, GPTQ, or LoRA distillation and validate quality deltas on your eval suite. Ensure savings do not degrade must-have metrics like hallucination rate.

Use Kubernetes with KEDA or Ray Serve and maintain hot replicas for bursty traffic. Tune prefetch and max batch sizes to balance latency and utilization.

Cache deterministic outputs and deduplicate embedding requests using content hashes. Set TTLs based on data drift to cut compute costs without sacrificing relevance.

Instrument with OpenTelemetry, Prometheus, and structured logs. Alert on P95 latency, token errors, and vector DB timeouts to prevent churn during trials.

Set per-plan TPS and token quotas using an API gateway and token-based metering. Communicate limits clearly to avoid surprise outages during evaluations.

Offer Python, Node, and Go clients with retries, idempotency keys, and streaming support. Developers convert faster when boilerplate and error handling are solved.

Provide Colab and Jupyter notebooks with sample datasets and environment setup. Reduce time to first success to improve conversion from signups to active users.

Let prospects experiment with RAG sources, temperature, and system prompts, then export code snippets. This accelerates POCs and internal stakeholder demos.

Use PostHog or Segment to track key actions like first successful API call and eval run. Trigger lifecycle emails or in-app guides to nudge completion.

Provide restricted keys and rotate on leakage signals. Enterprises expect clear key management and audit trails before committing to pilots.

Offer recipes for LangChain, LlamaIndex, Airflow DAGs, and Vertex or SageMaker pipelines. Meeting teams where they are reduces integration time and churn risk.

Publish on AWS, GCP, or Azure marketplaces to leverage existing budgets and legal frameworks. This can shorten enterprise sales cycles significantly.

Expose per-1k token, embedding, or vector operation costs and show scenario calculators. Transparency reduces sticker shock and helps self-serve teams forecast spend.

Use Stripe Metered Billing or similar, aggregate usage with idempotent webhooks, and reconcile late events. Trust in billing is a prerequisite for scale.

Run 30-60 day POCs with defined success metrics, dataset access, and weekly check-ins. End with a go-live plan covering scale, SLAs, and procurement steps.

Bundle SOC 2 Type II, pen test summaries, data flow diagrams, DPA, and DPIA templates. Ready-to-share security docs speed legal and security reviews.

Show measurable outcomes like reduced manual review hours or higher ticket deflection. Provide a calculator prospects can adapt to their volumes.