Your product.
Now AI-enabled.
Not an AI wrapper — a production AI team. We design, build, and run LLM features inside your existing product, governed by the same six gates as everything we ship. First AI feature live in under two weeks.
Add AI without turning your product into an experiment.
The failure mode for AI in production isn't hallucination — it's governance. Products fail when AI features ship without evaluation suites, without fallback logic, without human-in-the-loop checkpoints. Here's how we prevent that.
Step one is an AI readiness assessment — your data, your use cases, your constraints. We score each for production viability before committing a single line. Use cases that won't survive real users get cut, in writing, before they cost you anything.
Every AI feature ships with an evaluation suite: quality metrics, latency baselines, cost envelopes. You don't find out the AI is wrong in user feedback — you find out in the eval before it ships.
AI calls fail, models drift, costs spike. Every production AI feature is wrapped in fallback logic — graceful degradation to deterministic behaviour when the AI path fails. Your product keeps working either way.
AI-generated code — including AI feature code — passes the same six gates as everything we ship: quality scan, security scan, architect review, staging. Production AI governed the same way as production software.
Everything between “we should add AI” and shipped.
Claude, GPT, Gemini, or open-source — embedded in your product with prompt architecture, guardrails, and cost controls designed in, not bolted on.
Your data, retrieved and grounded: ingestion, chunking, embeddings, vector stores, and evaluation — so answers cite your truth, not the model's guesses.
Multi-step agents that take real actions — AI SDRs, support triage, document processing — with human-in-the-loop checkpoints where it matters.
When prompting isn't enough: dataset curation, fine-tuning, and distillation for your domain, benchmarked against the base model before anyone celebrates.
Search that understands, summaries that hold up, personalization that converts — product features users feel, not a chatbot in the corner.
Evaluation suites, drift detection, cost and latency dashboards, fallback chains. AI in production is an operations discipline — we run it as one.
How we price it: every AI engagement is scoped per project — we assess your use cases, then commit to a fixed scope and timeline before work starts. See development-pod and fixed-scope options on the pricing page →
AI Workflow Automation Studio.
Repetitive workflows are the highest-ROI target for production AI. We design, build, and govern AI automations that run inside your existing systems — not as a sidecar SaaS, but as production-grade features your team actually relies on.
Contracts, invoices, intake forms, and clinical documents — structured extraction with human-in-the-loop validation at decision boundaries. Accuracy benchmarked before launch.
Automated lead response, qualification scoring, and omnichannel handoff — WhatsApp, Meta, email. Agents that escalate to humans with full context, not dead-end bots.
Ticket classification, routing, and first-response generation — reducing first-response time and tier-1 volume while keeping humans accountable for edge cases.
RAG-powered assistants over your internal documentation, wikis, and data stores — answers that cite sources, stay in scope, and stay current as your docs change.
Natural-language query interfaces over your data — business intelligence that non-technical stakeholders can run without waiting on an analyst.
Multi-step agentic workflows with defined approval gates — from purchase-order review to compliance sign-off. Fully auditable, with human override at every critical step.
Real problems, real industries.
Lead qualification, instant WhatsApp/Meta response, and handoff to human agents — the Kenlo LYA pattern, below.
Document-grounded risk assessment and anomaly flagging with audit trails regulators can follow.
Intake summarization, coding assistance, and structured extraction — privacy-first, human-verified.
Recommendations and merchandising that learn from behavior — measured in conversion, not demos.
An assistant inside your product that knows your user's data and your domain — grounded, scoped, safe.
Clause extraction, comparison, and review queues that turn days of reading into minutes of checking.
No research theatre.
Every step has an exit criterion and a date. If a use case won't survive production, we tell you at step one — not after six months of demos.
AI readiness assessment
Your data, your use cases, your constraints — scored for feasibility and ROI before anything is built.
exit: a ranked list of use cases worth building, and the ones that aren'tArchitecture & stack selection
Model choice, retrieval design, guardrails, cost envelope — decided by architects, written down, challengeable.
exit: an architecture document your engineers can interrogatePrototype & validate
A working slice against real data inside two weeks, with an evaluation suite — not a slideware demo.
exit: measured quality numbers, go/no-goProduction integration
Into your actual codebase through the six-gate governance pipeline — security, fallbacks, rate limits, the boring things that make it real.
exit: the feature live for real usersEvaluation & optimization
Continuous evals, prompt and retrieval tuning, cost/latency optimization against the baselines from step three.
exit: quality up, cost down, in writingOngoing AI ops
Drift detection, model upgrades, incident response — 24/7 production support for production-instance issues.
ongoing: your AI keeps getting better after launchModel-agnostic by design — the right model per task, swappable as the frontier moves. Your stack preferences win, as always.
Not an AI wrapper. A production AI team.
If it can't survive real users, real load, and a security review, we don't ship it — and we tell you before building it.
We sell outcomes, not a model partnership. The best model for the task today, swapped without rework when tomorrow's is better.
Your repos, your cloud, your data boundaries — AI features built inside your product by architects who know it, not a sidecar SaaS.
Response quality, conversion, hours saved — every AI feature carries a metric and an evaluation suite from day one.
What we do not ship. And why that matters.
Saying no to the wrong thing is as important as saying yes to the right one. These are the AI patterns we refuse to ship, regardless of deadline or budget.
Any AI feature we build ships with evaluation, monitoring, and alerting. An LLM call without observability is a ticking clock we won't start for you.
If we can't trace the data the AI is grounded in, we won't ship a feature that depends on it being accurate. Garbage in still means garbage out — regardless of the model.
Agentic workflows that take real-world actions — send emails, make changes, post content — always include human-in-the-loop checkpoints at decision boundaries. Autonomous doesn't mean unsupervised.
Healthcare, fintech, legal, and HR use cases involving regulated decisions get a compliance review before architecture. We'd rather slow down at step one than build something that can't legally ship.
Models have downtime, rate limits, and cost spikes. Every LLM call we write has a fallback path — deterministic, cached, or degraded-but-functional. Your product keeps working when the model doesn't.
Straight answers on AI governance.
Three layers: first, every AI feature has an evaluation suite that tests output quality against labeled examples before any merge. Second, architect review on every AI-generated diff. Third, post-launch drift monitoring that alerts us when output quality degrades. Hallucinations that survive eval and architect review and drift monitoring are rare — and when they occur, the monitoring catches them before they compound.
We are. Full stop. Architect ownership doesn't end at launch — every AI feature we ship is monitored by the same team that built it. If an output causes a production incident, we investigate, remediate, and update the evaluation suite so it doesn't recur. This isn't in the fine print — it's in how we scope and price every engagement.
No — and anyone who does is selling you something. What we can guarantee: an evaluation framework that establishes your accuracy baseline, a monitoring system that alerts when it degrades, and a team that responds when it does. AI in production is an operations discipline. We run it like one.
Kenlo LYA — an AI SDR with full omnichannel, live in Brazil.
For one of Brazil's largest real-estate platforms, we built LYA: an AI sales agent that qualifies leads and responds instantly across WhatsApp and Meta channels, hands off to human agents with full context, and runs inside Kenlo's enterprise platform — built with AI automations across the board, shipped at lightning speed.
This page isn't a capability claim. It's a description of work that's running right now.
Your first AI feature, live in under two weeks.
One call with an AI architect — not sales. Response within 24 hours. After the architecture call, a ranked use-case list and two-week plan arrive within 4 business hours.