Put AI into production, owned by your team.
AI Implementation is how we get AI features and marketing automation into production, built alongside your own engineers. You end up with production code in your repos, behind your feature flags, plus eval suites, runbooks and a handover your engineers own.
There are two offers. One is AI product and feature development, taking an agent workflow all the way to a shipped feature. The other is marketing automation run by agents your team can audit. We build both with your engineers, in your repos, behind your flags, and we don't build throwaway prototypes.
Most AI demos never make it to production.
Enterprise AI product work usually stalls somewhere between the demo and the deploy. Often there are no evals, no one can see what the system does once it's live, and it has no clear owner. We start from your production constraints, which is why our pilots hold up when they meet your infrastructure.
We write production code in your stack, next to your engineers. When we leave, the system stays, and so does your team's ability to extend it.
Twelve weeks from demo to deploy.
Wedge selection
We pick the feature with the shortest path to user value, then check that the data, latency and risk profile can support it. Bigger ideas wait until this one works.
Production pilot
Built in your repos, behind feature flags, with real users. Usage telemetry is on from the first week, so launch day holds no surprises.
Harden
Eval suites, guardrails, observability and cost controls. It's dull work, and it's what keeps a feature from turning into a liability.
Handover
Your engineers own it from here. We document the architecture, pair with them through the transition, and then step back.
Features your competitors can't buy from a vendor.
AI features ship in quarters instead of years, owned by your team and running on your infrastructure. The market sees the product move faster, and each release makes the next one easier to build.
Grow marketing output without adding headcount.
Agents your team can audit run lifecycle, CRM and content operations. The hours they free up go back to work that needs a person.
Automation your auditors will sign off on.
Black-box automation runs into brand risk, compliance and regional differences as soon as it meets a real enterprise. What works is automation with approval gates, audit trails, and people placed where their judgment counts.
We build agent systems in stages: shadow mode, then human approval, then supervised autonomy. By the time an agent runs on its own, there's evidence behind the decision to let it.
A staged rollout, then weekly audits.
Workflow census
We map every recurring campaign and workflow across the org and cost each one in hours, error rate and cycle time. The board sees the savings measured against that baseline.
Agent design
We build agents for the highest-volume workflows first. Approval gates and brand guardrails go in from the start, before an incident forces anyone to add them.
Staged rollout
Shadow mode, then human approval, then supervised autonomy, one region at a time. Every action is logged and every escalation goes to a named owner.
Audit & expand
Weekly audits, wider coverage each month, and a quarterly hours-returned report your CFO can take to the board.
Questions about AI Implementation.
Something else? Email hello@innovaite.com or book the free 30-minute call.
What does AI Implementation include?
Two offers. One is AI product and feature development, taking an agent workflow all the way to a shipped feature. The other is marketing automation run by agents your team can audit. We build both with your engineers, in your repos, behind your flags.
How long does AI product development take?
One quarter. Weeks 1 and 2 go to wedge selection, weeks 3 to 8 to a production pilot, and weeks 9 to 12 to hardening. Then we hand it over to your engineers.
Who owns the code?
Your team does. We write production code in your stack, working alongside your engineers. At handover we document the architecture, pair with them through the transition, and step back.
How does marketing automation stay safe?
We roll agents out in stages: shadow mode first, then human approval, then supervised autonomy, one region at a time. Every action is logged, and every escalation goes to a named owner.
How do you measure the results?
We start with a workflow census that puts a cost on each recurring workflow in hours, error rate and cycle time. We report results against that baseline, including a quarterly report on hours returned.
Freeing up hours costs less than hiring for them.
Returning hours at scale cuts cost without layoffs. The capacity moves to strategy work, every agent action is audited, and we report each quarter against a baseline your CFO signed off on.