vendorAIdue diligence

Checklist: What to Ask AI Vendors When They Promise Contact Enrichment

UUnknown

2026-02-22

10 min read

Practical due diligence checklist for teams vetting AI contact enrichment — accuracy, provenance, privacy, integration, and SLAs for 2026.

Hook: Your pipeline is only as good as the contacts inside it — and AI enrichment promises miracles. But will it deliver?

Marketing and sales teams in 2026 face a familiar but sharper pain: contact data scattered across forms, spreadsheets, and tools, inflated bounce rates from unverified lists, and rising compliance risk as privacy rules multiply. AI enrichment vendors claim to fix this with fast, smart augmentation. Before you hand over your lead stream, run this due diligence checklist to validate their accuracy benchmarks, data provenance, privacy posture, and integration readiness.

Why this checklist matters now (short answer)

By late 2025 and into 2026 the market shifted: commodity AI models are everywhere, but the difference between vendors is increasingly in the plumbing — how they prove accuracy, track data lineage, manage consent, and integrate with your stack. Buying the wrong AI enrichment provider creates tech debt, harms deliverability, and can expose you to regulatory penalties. Use this checklist to separate marketing claims from operational reality.

Key trends (2025–2026) that change the evaluation criteria

Regulatory tightening: EU AI Act guidelines and expanded U.S. state privacy regimes (CPRA extensions, VA/CDPA updates) pushed vendors to improve explainability and DPA controls during 2024–2025.
Provenance-first buyers: Enterprises now expect record-level lineage and consent timestamps, not just aggregated assertions.
Model transparency: Buyers favor vendors that combine LLM outputs with deterministic data sources and human review to limit hallucination.
Integration fatigue: Marketing stacks are crowded — teams prioritize vendors that reduce connectors and centralize hygiene workflows.

Checklist overview: Four pillars to test every AI enrichment vendor

Structure your vendor RFP and trial around these pillars. For each item below, request evidence: logs, sample data, SLA clauses, and a short pilot plan.

Accuracy & Benchmarks
Data Provenance & Explainability
Privacy, Compliance & Security
Integration, SLA & Operational Fit

1) Accuracy & benchmarks — don’t accept marketing fuzz

Ask for empirical evidence. Vendors love to quote accuracy numbers; the value is in how they measure and validate them.

Questions to ask

What specific accuracy metrics do you provide for each enrichment type (email validity, phone normalization, job title parsing, company match, intent signals)?
Can you supply a time-stamped accuracy report from an independent third-party audit or a reproducible test dataset?
How do you define accuracy for probabilistic outputs (e.g., inferred industry or seniority)? Do you report precision, recall, and confidence intervals?
What is your baseline for email validation vs. inbox delivery? Do you track both syntax/SMTP checks and downstream bounce/delivery rates?
Do you support blind A/B pilot testing so we can measure impact on conversion, deliverability, and outbound engagement?

Acceptance criteria (examples)

Parsing/normalization accuracy ≥ 95% on vendor-supplied test sets for structured fields.
Confidence scoring that maps to empirical outcomes — e.g., contacts with confidence ≥ 0.9 should produce < 5% hard bounce in our mail streams.
Independent validation or customer case study demonstrating measurable deliverability or conversion lift after enrichment.

2) Data provenance & explainability — chain-of-trust is non-negotiable

In 2026 buyers expect record-level transparency. Ask not only what the vendor returns, but why they returned it.

Questions to ask

For every enriched field, can you supply the source(s), timestamp of last verification, and a transformation log?
Do you tag data that was inferred (model output) vs. observed (public record, opt-in database)? How is synthetic or third-party modeled data labeled?
Can you provide a human-review flag or provenance chain when the model's confidence is low?
Do you support cryptographic proofs or signed attestations for source records (useful for audits)?
How do you handle conflicting sources — do you surface all sources and let our logic choose, or do you canonicalize on your side?

Why this matters

Vendors that provide detailed provenance reduce operational friction: your ops team can set rules by source, prioritize high-confidence records, and quickly audit any dispute. Provenance also underpins compliance with data subject requests in many jurisdictions.

3) Privacy, compliance & security — ask for the documents, not the soundbites

Privacy compliance is table stakes. In 2026, expect vendors to support modern privacy workflows and provide audit-friendly controls.

Questions to ask

Do you have a Data Processing Agreement (DPA) available? Can you list all subprocessors and their locations?
How do you capture and store consent? Do you provide per-record consent metadata and timestamps to support DSARs?
Where is data stored and processed (region, cloud provider)? Do you offer VPC, dedicated tenancy, or on-prem deployments for sensitive customers?
What certifications and attestations do you maintain (SOC 2 Type II, ISO 27001, PCI where applicable)? Can you share recent reports under NDA?
How do you comply with the EU AI Act and other relevant guidelines for high-risk AI systems? Do you provide model cards and impact assessments?
How do you prevent the vendor from retaining or reusing customer data to train models, or is that included in the contract with opt-out options?

Red flags

No DPA or refusal to list subprocessors.
Blanket claims that “we don’t store data” without technical proof (e.g., ephemeral processing architecture, deletion logs).
Vendor trains models on customer data by default with no opt-out or compensation.

4) Integration, SLA & operational fit — this is where pilots live or die

Even a high-accuracy product can fail if it won’t plug into your CRM, ESP, or ETL pipeline. Prioritize vendors that reduce operational touchpoints and offer clear SLAs.

Questions to ask

Which CRM and ESP connectors do you support natively (Salesforce, HubSpot, Marketo, Klaviyo)? Are connectors maintained or community-built?
Do you offer both real-time enrichment (API/webhook) and batch enrichment (SFTP, bulk API)? What are your rate limits and SLA latencies?
Can you provide sample integration flows and field mapping templates for our stack?
What is your SLA for uptime, latency, and data accuracy? Do you include remediation credits if accuracy or availability targets are missed?
How do you handle backfills and historical enrichment? Are there limits or additional costs for large backfill jobs?
What monitoring and alerting do you provide (webhooks for failures, dashboards for enrichment rates, audit logs)?

Operational acceptance criteria

Real-time API latency < 300ms for synchronous enrichments, or clear SLOs for asynchronous jobs.
Out-of-the-box connectors for top CRMs with maintained mapping templates.
Audit logs and error webhooks to integrate with your observability stack.

SLA and contract clauses to negotiate

Don’t treat the SLA as an afterthought. Push for measurable commitments.

Accuracy SLAs: Define measurable metrics (e.g., syntactic email validity, company match rate) and remediation steps if thresholds aren’t met.
Uptime & latency: 99.9% uptime for APIs and defined latency SLOs for real-time enrichment.
Data deletion & return: Require certified deletion on termination and a machine-readable export of all enriched and provenance data.
Audit rights: Right to audit subprocessors and see SOC/ISO reports under NDA.
Liability and indemnity: Clarify responsibilities for data breaches, regulatory fines, and deliverability losses caused by bad data.

Pricing models & TCO — beyond per-record costs

Per-record pricing hides complexity. Map costs to real operational events.

Price model questions

Is pricing per enrichment, per field, or credit-based? How do you price re-checks or re-enrichments?
Are there overage costs for spikes? How are rate limits handled during batch jobs or backfills?
Do you charge for provenance metadata or audit logs export?
What support tiers exist and are they included or extra?

Calculate TCO with these factors

Cost per enrichment + expected volume (including retries and revalidations).
Integration and engineering setup hours to map fields and build webhooks.
Ongoing ops hours for exceptions and manual review.
Deliverability savings (reduced bounce rates, ISP reputation improvement) and conversion lift; quantify expected revenue impact for a 6–12 month payback.

Testing plan: Run a controlled pilot

Don’t buy blind. A short, controlled pilot reveals gaps quickly.

Pilot design (30–60 days)

Run a blind enrichment on a representative sample (5–10k records) with two vendors or with an internal baseline.
Measure: parsing accuracy, email/smtp validation, phone normalization, match rate to company database, and confidence distribution.
Execute a live send to a small segmented audience to measure deliverability impact and hard bounces.
Evaluate provenance logs — can your team trace the chain for any disputed record within 15 minutes?
Run a compliance dry-run: respond to a simulated DSAR request and measure time-to-complete with vendor support.

Success metrics to require for pilot completion

Clear provenance for ≥ 95% of enriched records.
Confidence scores aligned to mail bounce rates in your environment.
Integration tests completed with no critical data mapping errors.
Support SLAs met for escalation within pilot period.

Operational playbook: How to use the vendor after selection

Adopt an operations-first approach to preserve list hygiene and deliverability.

Define enrichment policies by source and confidence — e.g., only route contacts with confidence ≥ 0.8 into nurture; low-confidence records go to manual review.
Automate periodic re-validation windows (30/90/180 days) based on contact age and engagement.
Log provenance data in your CRM custom fields, not as an opaque vendor blob — this enables segmentation and troubleshooting.
Use soft opt-in flows and consent synchronization for markets with strict opt-in requirements.

Red flags to walk away from

No test data or refusal to run a blind pilot.
Opaque pricing and hidden fees for backfills or provenance exports.
No per-record provenance, no DPA, or refusal to sign audit clauses.
Claims of perfect accuracy without independent evidence or human review fallback.
Vendor trains models on your data by default and won’t offer an opt-out.

Short case example (anonymized, operational lessons)

A mid-market SaaS in 2025 ran two enrichment pilots. Vendor A returned high match rates but with no provenance and used customer data to improve models. Vendor B provided detailed source logs, per-record confidence, and an opt-out for training. After a 45-day test the SaaS found Vendor B reduced hard bounces by ~38% on warmed lists and cut manual review time by 60% because operations could triage by provenance and confidence. The company chose Vendor B — not due to marginally higher cost, but because of lower ongoing ops time and reduced regulatory risk.

Quick-reference checklist (copyable for RFPs)

Provide independent accuracy reports and confidence-to-outcome mappings.
Include per-record provenance: sources, timestamps, transform logs, model vs. observed flag.
Supply DPA, subprocessor list, and security attestations (SOC2/ISO).
Offer VPC/dedicated tenancy and data deletion certification on termination.
Support real-time APIs + batch with documented rate limits and latency SLOs.
Deliver audit logs, DSAR support, and opt-out for training on customer data.
Define accuracy and uptime SLAs with remediation credits.
Run a 30–60 day pilot with blind testing and live deliverability checks.

Final takeaways — how to decide

In 2026 the vendor landscape rewards operational transparency more than marketing claims. Prioritize vendors that can demonstrate real-world accuracy with independent validation, provide record-level provenance and consent metadata, offer enterprise-grade security and deployment options, and integrate cleanly into your CRM and ESP. The right partner reduces manual cleanups, preserves deliverability, and lowers compliance risk — all measurable outcomes that justify the investment.

Call to action

If you're evaluating AI enrichment vendors this quarter, use our downloadable RFP template and pilot-run checklist to standardize vendor responses and speed selection. Want a pre-filled scorecard tailored to your stack (Salesforce/HubSpot/Klaviyo)? Contact our team for a free 30-minute vendor-scoping session and get a custom checklist that matches your compliance and deliverability goals.

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