MIME Parsing Checklist for Customer Support Teams

Agree on address conventions like support+refunds@ and support+vip@ to pre-route messages and simplify queue mapping. Document how aliases map to teams, skills, and business hours.

Collect sample emails from Gmail, Outlook, Apple Mail, mobile clients, and popular CRM notifiers to capture MIME variations. Use these samples as your baseline parsing corpus.

If you need near real-time triage, use webhooks with backoff and idempotency; for bursty queues, consider REST polling with rate limits. Document SLAs and retry budgets for each path.

Embed ticket IDs in Subject like [TKT-12345] and in References headers to strengthen thread linkage. Publish guidelines in agent macros and auto-reply templates.

Use the earliest trusted Received header timestamp, normalize to UTC, and store alongside message-id. This ensures consistent SLA calculations across time zones and relays.

Create a routing table that links To/Cc addresses to product lines, languages, or tiered support. Review quarterly as teams and SLAs change.

Convert quoted-printable and base64 bodies to UTF-8 while preserving original encodings in metadata. This prevents mojibake and improves search and sentiment accuracy.

Prefer text/plain for NLP tasks and fall back to HTML-to-text if plain is missing. Trim signatures and quoted replies using standard delimiters to reduce noise.

Map Content-ID references in HTML to attachment parts and tag which are inline vs downloadable. Store content-disposition, content-type, and file size for policy checks.

Store the original RFC 5322 message alongside a schema-versioned JSON record. This supports reprocessing, auditing, and safe migrations when parsers improve.

Extract Message-ID, In-Reply-To, and References, and compute a canonical thread key. Expose a thread confidence score to downstream routing and UI.

Identify PGP or S/MIME parts and flag messages that require special handling or pass-through. Preserve signature blocks for compliance while providing an agent-safe preview.

Drop or merge duplicates using Message-ID, X-Original-Message-ID, and a normalized body hash. Prevents ticket storms from retransmits and forwarding loops.

Combine sender domain, alias, and keyword signals to assign queues and priorities. Include language detection to direct non-English emails to the right agents.

Start SLA on first customer message, pause on agent replies, and resume on customer follow-ups using parsed headers and thread keys. Write events to an auditable timeline.

Use Auto-Submitted, Precedence, and common patterns to flag vacation replies and mailer-daemon bounces. Exclude these from SLA and CSAT metrics automatically.

Scan attachments for malware, enforce size and type limits, and generate PDF or image previews for risky formats. Improves agent safety and reduces download friction.

Score tone from text/plain content using lexicons, caps ratio, and punctuation density, then boost priority for escalations. Use cautiously and allow agent override.

Verify authentication results and expose them in JSON and the agent UI. Use failures to trigger fraud warnings or stricter link-safety rules.

Sign payloads with shared secrets, validate timestamps, and implement exponential backoff with jitter. Use idempotency keys to prevent duplicate ticket creation.

Restrict access to raw MIME, headers, and attachments to least privilege, and redact tokens or API keys in logs. Track access in audit trails.

Use pattern matching and OCR on PDFs or images to remove sensitive data like card numbers before indexing or display. Keep a reversible vault only when legally required.

Apply differential retention for regulated queues and auto-delete raw MIME after the configured window. Store only derived fields needed for analytics and SLAs.

Decode RFC 2047 encoded headers and normalize right-to-left scripts to preserve readability. Ensure search and routing work across unicode languages.

Include multipart/mixed, alternative, related, malformed boundaries, and nested messages/rfc822 in your tests. Add real-world examples from escalations and high-volume senders.

Version your JSON schema and run CI checks that validate example payloads for breaking changes. Share fixtures with downstream consumers to prevent integration drift.

Track failures by content-type, boundary errors, and sending client to pinpoint regressions. Alert when error rate or unknown part types spike.

Measure time-to-first-view, reroute count, duplicate suppression rate, and attachment policy hits. Tie improvements back to parsing quality and routing rules.

Re-deliver stored MIME into a non-production environment to reproduce bugs and verify fixes. Tag replays to avoid polluting analytics and SLAs.

Use streaming parsers where possible, batch REST polling, and apply consumer concurrency with backpressure to protect downstream systems. Load test using your peak-hour corpus.