Claude implementation consulting that ships, not slides.

Most Claude consulting engagements end in a 40-page report nobody acts on. Ours end in a working system your engineers can extend. The deliverable is code, evals, runbooks, dashboards, and a written handoff, not a strategy deck.

Every TechTide AI consulting engagement carries:

• A named price. $1,000, $5,000, $25,000-$40,000. No hourly billing, no surprise retainers.
• A named timeline. 48 hours, 7 days, 21 days. We hold the line on scope to hit the date.
• A named failure mode. We tell you in writing where each system breaks before we ship it.
• A go/no-go gate. If the work isn't worth shipping, we say so in writing and refund the difference.

The result is a Claude implementation your engineering team owns. Not a vendor dependency. Not an off-the-shelf wrapper. Production code under your auth boundary, in your repo, with your team's name on the runbook.

Typical engagements come from three buyers:

• CTOs with a stuck pilot. The pilot demoed well six months ago, the team built around Claude, and the launch keeps slipping. We run Production Triage in 7 days and tell you whether it's worth saving.
• AI task-force owners with a deadline. Three weeks until a board demo. The MCP integrations are leaking. We run Workflow Rescue and ship one workflow to production with hardened tools, regression evals, and observability.
• Engineering leaders evaluating before they buy. They start with the $1,000 AI Production Readiness Audit. 48 hours later they have a clear go/no-go and a sequenced fix list.

We don't work with teams looking for an AI strategy facilitator, an off-the-shelf chatbot reseller, or vendor-selection theater. The work is implementation.

Same five-step framework on every engagement, scaled to the offer size:

1. Architecture audit. Read the code, prompts, MCP servers, retrieval layer, observability stack. Find the load-bearing fragility.
2. Permission and token review. Every MCP tool gets a scope check. Every model call gets a cost review. Findings written down.
3. Eval suite. No production pilot ships without one. We build a regression suite you can re-run weekly to catch drift.
4. Hardening. Scoped auth, rate limits, audit logs, output validation. The reviewer queue is the last gate before any tool action.
5. Handoff. Pair programming sessions, runbook, dashboards wired to your stack, and a written architecture diagram your team owns.

The same pattern shipped Stripe Connect v2 reconciliation in 14 days, an Intune policy rollout assistant in 18 days, and a real-estate MLS data integration in 3 days. See the full case studies on the Case Files page.

• Production code in your repo, under your auth, with your engineers paired into the build.
• Eval suite wired to CI, re-runs weekly, catches model drift and prompt regressions.
• Observability dashboards in your existing stack (Datadog, Grafana, Honeycomb).
• MCP servers with scoped OAuth, typed tools, rate limits, audit logs, output validation.
• Runbook covering rollout, rollback, on-call escalation, named failure modes.
• Architecture diagram and Loom walkthrough archived in the case file.