Two weeks. Integration map, risk audit per surface, build-vs-buy decisions, kill list, six-month rollout plan.
/ Conversational AI Consulting /
Conversational AI consulting for teams that ship, not teams that deliberate.
Go beyond the demo. We build resilient, secure, and highly-capable conversational AI agents that integrate deeply with your existing systems.
Big systems integrators hand you a playbook. We hand you a running system.
Typical Chatbot Vendors
Hundred-page playbooks delivered by a team of analysts you will never meet again
Multi-quarter platform evaluation phases that compare seven vendors and recommend none
A junior layer writing webhooks you will never see or own
Pilots that run inconclusively for six months because success metrics were never defined
A handoff that leaves your team under-trained and dependent
JAAX Agents
One integration live in your stack in fourteen days, with an eval harness (automated tests that score model output) your team can read
An integration strategy sprint that names which surfaces to wire first and which to kill
The same two engineers on your kickoff call are on your production cutover
Idempotent webhooks, rate-limit handling, cost controls, and monitoring shipped before we declare done
Senior engineers you can text on a Saturday when an alert fires
Four steps. Every time. No exceptions.
Step 1
Use-case triage
Which integrations actually pay off and which drain resources before shipping. Written assessment per surface against a fixed rubric - webhook availability, data sensitivity, integration cost, signal quality (is the data clean enough to learn from), metric ownership. Output: reds, yellows, and greens.
Step 2
Eval before prompt
The eval is the spec. Write a hand-rated golden set of 20–40 examples per critical intent before we wire a single webhook or write a single prompt. This habit is what lets us ship production integrations in days instead of weeks.
Step 3
Production hardening
Idempotent webhook handlers, retry queues, automatic throttle handling so a vendor's API limits don't take down your workflow, cost caps per inference, redaction that strips personal data before it reaches the AI model, feature flags, and a dashboard that ships before the integration is interesting. Every integration ships instrumented or it does not ship.
Step 4
Team enablement
Runbooks for incident response and rollback, eval harnesses your team owns, training, and the README every junior engineer can read on a Monday. The goal is self-sufficiency. We leave when the system runs without us.
/ What you actually get /
One integration live in your stack. Eval harness. Dashboard. Runbook.
Every engagement ships with a production-grade integration living in your stack, an eval harness your team can inspect, and a dashboard that shows latency, error rate, refusal rate, and cost. The runbook is yours. The system runs without us.
Book a fit call →AI Integration Sprint · Deliverable · JAAX Labs
Integration Sprint - Deliverable
01
Integration Strategy Map
Prioritized list of surfaces with risk audit per integration - webhook availability, rate-limit budget, data sensitivity, integration cost, signal quality.
02
Build-vs-Buy Decisions
Per-surface recommendation with named vendors, integration cost, and the migration path if you change your mind in six months.
03
Six-Month Rollout Plan
Named integrations sequenced with owners assigned, risks identified, and success metrics per surface.
04
Kill List
Integrations that won't pay off, with one-paragraph justification. The surfaces we recommend you stop spending on.
/ Engagements & pricing /
Four shapes. Ranges from $25k to $150k+.
The other three are the natural next steps - most clients who buy the sprint commission at least one within the quarter. We publish ranges because we hate the call where you ask the price and three weeks of email-tag begin.
Fourteen days to one integration live in your stack. Webhook plumbing, eval harness, monitoring dashboard, runbook included. Refundable if it doesn't ship.
Six to twelve weeks. Multi-system integration, retry queues, cost controls, PII redaction, monitoring, drift detection, audit logs, team handoff.
Senior engineers embedded with your platform team. Architecture review, eval design, webhook code review, on-call escalation, hiring help.
/ How we know this works - Sentinel /
The integrations we sell power our own live product.
Sentinel is JAAX's live Shopify analytics product. We have on-call rotations on it. We have rolled back integrations in it. We have eaten the cost of a runaway inference loop and rewritten the cost cap the same week. The engineering muscle that runs Sentinel is the same muscle that runs your engagement.
See Sentinel
12
integrations in production
92%
renewal rate
14d
typical first integration
2
founders, no juniors
/ Who this is for /
For the team that has a demo that works and a deployment that stalls.
The buyers we do our best work for share three traits:
- A number they want moved - deflection rate, recovery rate, time-to-quote, cost-per-ticket
- At least one AI initiative already attempted - they know the difference between a working agent and a working demo
- A window, usually a quarter, to show something running
Typical buyers are heads of platform engineering, RevOps, or digital transformation at mid-market and enterprise companies - roughly $50M to $500M in revenue, sometimes larger inside a single division.
The work spans four shapes:
- A HubSpot-native company integrating an AI qualification agent into the lead pipeline
- A Zendesk shop wiring a deflection layer in front of Tier 1 support tickets
- A Shopify merchant connecting the AI model (LLM) to Stripe disputes and order data
- A Snowflake team putting a natural-language query layer on top of operator metrics
If you need a hundred-page playbook, a multi-quarter platform evaluation phase, or an AI center of excellence with a steering committee - call one of the four firms that sells that. We're not better at that than they are, and we'll tell you so on the fit call.
"If a webhook is not idempotent, it is not in production. It is a coin flip in production. There is a difference."
From the JAAX methodology
Questions we get on every fit call.
AI integration is the engineering work of wiring an AI capability into the systems your business already runs on - your CRM, support desk, ecommerce platform, billing system, data warehouse, internal tools. AI consulting decides which AI projects to do; AI development builds the model or the agent itself; AI integration is the plumbing that makes the model show up inside the workflow that pays the bills. Most engagements that fail in production fail at integration - the model worked, the webhook didn't, or the rate limit blew up the queue at month-end.
The stacks we have shipped against most often are HubSpot, Salesforce, Zendesk, Intercom, Shopify, Stripe, Slack, Notion, Linear, Postgres, Snowflake, BigQuery, and the usual suspects on the warehouse side. We integrate against documented APIs and webhooks; for legacy systems without either we build a thin adapter layer rather than a "platform." If a CRM you use is not on that list, ask - most enterprise systems publish enough surface area to integrate against safely; the work is in handling the edges.
An integration strategy sprint is two weeks, no extensions. A production proof-of-concept is fourteen days from kickoff to one integration live in your stack. A full implementation across multiple systems is six to twelve weeks depending on how many surfaces and how clean the existing webhook hygiene is. We refuse engagements that don't fit a 14-day sprint at the unit level. If the work cannot be sliced into 14-day deliverables, we have not finished scoping it.
Strategy sprints run $25–45k. Production proofs-of-concept run $50–150k. Full implementations start at $150k and scale with integration surface, data sensitivity, and how many existing systems the AI has to touch. Embedded team augmentation is a monthly retainer. Pricing is uniform across our practice - we charge by engagement shape, not by domain.
Six habits, every time:
- Idempotent webhook handlers so retries don't double-process.
- Automatic throttle handling so a vendor's API limits don't take down your workflow.
- A hand-rated eval harness written before the prompt, so we can tell when output quality regresses.
- A cost cap per inference and per tenant, so a runaway loop doesn't drain a month's API budget overnight.
- A feature flag on every new path so we can roll back without a deploy.
- A dashboard that shows latency, error rate, refusal rate, and cost - shipped before the integration is interesting.
We handle the engineering side and we will not ship without the governance side. We strip personal data before it reaches the AI model, isolate per tenant, write audit logs the compliance team can read, route sensitive payloads through hardened model paths, and configure vendors not to store your data when sensitivity requires it. We do not write your data processing agreement (DPA) or your SOC 2 report - your security and legal teams own those - but we ship integrations that survive their review the first time, not the third.
Yes, mutual NDA before any technical conversation. We do not work for clients with conflicting active engagements in the same competitive set during a quarter - a rule we enforce on ourselves more strictly than most clients ask us to.
If you need a hundred-page integration playbook delivered by a team of analysts you will never meet again, hire one of the four firms that sells that. If you need an AI integration live in your stack by the end of the month, hire us. The two-person constraint is the feature. There is no junior layer writing webhooks you'll never see. The engineers writing the integration code are the engineers on your kickoff call, your production cutover, and your Saturday-night incident.
Send a paragraph. We'll come back the same day.
Tell us which system you want AI inside of and the number you want moved. We'll come back with a yes, a no, or a sharper question. No discovery deck, no pitch meeting marathon.
Book a 30-min fit call