Direct answer
Hire a production AI partner when you need RAG or agents that survive security review, evaluation gates, and real traffic, not a demo. Score vendors on shipped systems, compliance posture, senior delivery model, and an evaluation harness you can keep. Apptension ships senior-only teams with production AI proof: 1.5M+ calls/month, >98% PII detection accuracy, 360+ projects since 2012.
Insight: The hiring decision is not “who knows the latest model.” It is “who can prove the system still works after audit, incident, and day 30 of real users.”
This guide is the Production Evidence Test for CTOs and consulting engineering leaders shortlisting a generative AI or RAG delivery partner. Use it to replace vibe-based vendor lists with evidence you can defend in procurement.
_> Production AI proof
Public metrics you can cite
When you should hire a production AI partner
Build or buy internally when you already have senior AI engineers, a security-approved model gateway, and an evaluation harness in CI. Hire a partner when any of these are true:
- Security or risk will block a demo that cannot show audit trails, retention controls, and PII handling
- You need RAG or agents in a regulated SaaS surface within a fixed delivery window
- Your internal team is strong on product, but thin on production AI ops (eval gates, red teaming, cost controls)
- You need a senior-only pod that ships without handholding while your core team stays on the roadmap
If you are still exploring model quality on synthetic data, start with a narrow pilot. If you are selecting who owns production RAG for customer data, run the scorecard below.
The Production Evidence Test
Score each shortlisted vendor 0 to 2 on every row. 0 = claim only. 1 = partial evidence. 2 = artifact you can keep (repo sample, report, runbook, live metric).
| Criterion | What to demand | Pass signal | Fail signal |
|---|---|---|---|
| Shipped systems | Named production workloads with scale and latency | Live traffic numbers, architecture notes, on-call story | “We did a PoC” with no production metrics |
| Compliance posture | PII/PHI handling, retention, subprocessors, audit logs | Redaction pipeline, DSAR path, >98% style detection claims with method | Policy PDF with no operational evidence |
| Senior delivery model | Who writes and owns production code | Senior engineers only, self-directed delivery | Junior-heavy bench or opaque staffing |
| Evaluation harness | Regression evals you can keep after handoff | CI gates, golden sets, citation/quality thresholds | Manual “looks good” reviews only |
| Security review readiness | Threat model, injection tests, tool allowlists | Versioned controls and rollback plan | Prompt engineering as the only control |
| Cost and latency discipline | Budgets, caching, p95 targets | Measured cost per successful task | Unlimited token spend with no owner |
Key rule: A vendor that cannot leave you an evaluation harness is renting you a demo, not transferring production capability.
Partner shortlist workflow
_> From RFP noise to a defendable hire
→ Scroll to see all steps
What good evidence looks like
Use these prompts in diligence calls. They force concrete answers:
- Show a production path. “What is the highest monthly volume you have run for voice or RAG, and what broke first?”
- Show PII controls. “How do you detect and redact PII before model calls, and how do you measure detection accuracy?”
- Show evaluation ownership. “Which tests run on every prompt or model change, and who owns the golden set after handoff?”
- Show senior ownership. “Who is on the critical path for the first production release: names, seniority, and decision rights?”
Apptension’s public proof points map to those questions: senior-only delivery, production AI at 1.5M+ calls/month, >98% PII detection accuracy, and 360+ projects since 2012. See Generative AI Solutions for regulated SaaS for the service model, then review shipped work such as the real-time conversational AI avatar case study and the LEDA RAG analytics case study.
Artifacts to keep after handoff
_> Transfer production capability, not slideware
Golden evaluation set
Domain questions, edge cases, and pass thresholds owned by your team.
Trace schema
request_id, model version, retrieval IDs, policy decision, and human overrides.
PII redaction pipeline
Deterministic rules plus classifiers with measured accuracy and retention TTLs.
Incident runbook
Severity matrix, rollback for prompts and models, and update cadence.
Cost dashboard
Tokens, tool spend, and cost per successful task by tenant.
Architecture decision log
Why gateway, retrieval, and tool broker choices were made.
Build vs partner: a fast decision matrix
| Situation | Prefer internal | Prefer senior partner |
|---|---|---|
| Security review in < 8 weeks | You already have approved AI gateway patterns | You need a team that has passed similar reviews before |
| RAG over regulated corpus | Strong retrieval + eval ownership in-house | Need production RAG patterns and citation discipline fast |
| Voice or high-volume agents | Existing streaming and SRE capacity | Need proven latency and volume operating experience |
| Staffing model | Can free senior engineers for 2+ quarters | Need month-to-month senior capacity without hiring lag |
For deeper build-vs-buy framing, use the CTO playbook on build vs buy vs partner. For procurement depth, pair this page with the enterprise vendor due diligence checklist and the RAG quality evaluation guide.
Outcomes of evidence-based partner selection
Faster security sign-off
Evidence packs reduce questionnaire loops and unblock regulated go-lives.
Lower production risk
Eval gates and PII controls catch failures before customers do.
Cleaner handoff
You keep the harness, traces, and runbooks instead of depending on a black box.
Defendable spend
Senior-only delivery and measured ROI beat opaque bench inflation.
How Apptension fits the scorecard
Use the same table on Apptension. Do not take marketing language at face value. Ask for the artifacts.
- Shipped systems: production generative AI and conversational workloads, including high-volume voice paths
- Compliance posture: PII detection and guardrails designed for regulated SaaS, not bolted on after a demo
- Senior delivery: senior engineers only, self-directed teams, no juniors on client critical path
- Evaluation: quality, safety, cost, and latency treated as release gates, not slide metrics
Start with production generative AI for regulated SaaS, then inspect delivery proof in the conversational AI avatar case study and LEDA LLM exploratory data analysis case study. If you need embedded capacity after kickoff, see nearshore senior hybrid engineering teams.
Require evidence of shipped systems, compliance controls, senior delivery, and an evaluation harness you can keep. Score vendors 0 to 2 on each criterion and reject claim-only answers.
Hire when security review, regulated data, or go-live pressure exceeds your current senior AI capacity. Build when you already own gateway patterns, evals, and on-call for AI systems.
Production traffic metrics, PII handling with measured accuracy, audit-ready tracing, incident runbooks, and CI evaluation gates with golden sets.
A short scorecard that replaces vendor listicles with artifact-backed scoring across shipped systems, compliance, seniority, evaluation, security readiness, and cost discipline.
Production AI failures are usually systems failures. Senior engineers who own architecture, evals, and incidents reduce rework and audit risk versus opaque junior-heavy benches.
Conclusion
Choosing a production AI partner is a systems decision. Demand artifacts you can keep, score them honestly, and run a paid spike before you commit the roadmap.
If your next step is a shortlist conversation, bring this scorecard and ask Apptension to walk the same evidence path: talk to a senior engineer about your production AI scope.
