Remove manual work with AI that actually ships.
LLM-powered workflows, predictive models, computer vision and intelligent automation that removes repetitive work at scale — engineered by teams who deploy AI into production, not just prototype it.
What AI & Automation means at Kubetiq.
Most AI pilots never make it to production. We focus on the unglamorous parts — data pipelines, evaluation, monitoring, fallback logic — that decide whether an AI feature actually survives contact with real users and real edge cases.
Whether it's an LLM-powered support assistant, a document processing pipeline, or a predictive model driving business decisions, we design for reliability first. That means clear guardrails, human-in-the-loop escalation where it matters, and observability so you always know why the system did what it did.
Teams drowning in repetitive manual data entry or document review
Support or ops teams that need an AI assistant with real guardrails
Businesses wanting predictive insight from data they already collect
Everything under AI & Automation, in-house.
No subcontracted vendors — every capability below is delivered by our own team.
A modern, production-grade stack.
How a AI & Automation engagement runs.
Seven stages, one accountable team, weekly visibility throughout.
Discovery
We identify the highest-leverage manual process and define what success looks like numerically.
Data Audit
Your existing data is assessed for quality, volume and readiness before any model work begins.
Prototype
A working proof-of-concept is built against real data within the first sprint.
Development
Full pipeline build — ingestion, inference, guardrails and human-in-the-loop review.
Evaluation
Rigorous testing against edge cases and adversarial inputs before rollout.
Deployment
Phased rollout with monitoring dashboards and fallback logic in place.
Support & Iteration
Ongoing model monitoring, retraining and accuracy tracking post-launch.
Transparent starting packages.
Every project is scoped individually — these are indicative starting points.
- One automated workflow
- Pre-built LLM integration
- Basic monitoring dashboard
- 4-6 week delivery
- 30-day support
- Multiple automated workflows
- Custom fine-tuned models
- Human-in-the-loop review UI
- Analytics & accuracy tracking
- 90-day support
- Enterprise-scale AI platform
- Dedicated ML engineering pod
- Custom model training pipeline
- SLA-backed accuracy targets
- Ongoing retainer support
Every AI & Automation build ships with these by default.
Fast Inference
Low-latency pipelines so AI features feel instant, not sluggish.
Guardrailed Outputs
Validation and fallback logic so the model never operates unsupervised on risk.
Measurable Accuracy
Every model ships with evaluation metrics you can track over time.
Secure by Design
Your data never trains third-party foundation models without explicit consent.
Scalable Pipelines
Built to handle growing data volume without re-architecture.
Cloud Ready
Deployed on infrastructure that scales inference on demand.
Easy Maintenance
Clear monitoring so drift and failures are caught before users notice.
AI Ready
This is the AI-ready feature — built to extend, not a one-off script.
The team behind every build.
Dedicated Team
A senior team assigned to your project from kickoff to launch — not rotating juniors.
Experienced Engineers
Every build is led by engineers with 5+ years shipping production systems.
Transparent Communication
Weekly demos and direct access to the people building your product.
On-Time Delivery
Realistic scoping upfront so we don't need to renegotiate deadlines later.
Affordable Pricing
Transparent, milestone-based pricing with no hidden change-request markups.
Post-Launch Support
We own the outcome — if something breaks post-launch, we fix it, no extra invoice.
Custom Solutions
No templates stretched to fit. Every build starts from your actual requirements.
Common questions about AI & Automation.
Timelines depend on scope — a focused build can ship in 4-6 weeks, while a multi-module platform can run 3-4 months. You'll get a specific timeline as part of our proposal, not a vague range.
We quote fixed-price for well-defined scopes and time-and-materials for evolving work. Every proposal includes a milestone-by-milestone breakdown so you know exactly what you're paying for and when.
Both. A large share of our work is enterprise-scale, but we also partner with funded startups building their first product. What matters is a clear scope and a committed stakeholder on your end.
Yes. Once the final invoice is settled, the complete source code and infrastructure access are handed over to you. There's no vendor lock-in.
Every engagement includes a post-launch support window, and we offer ongoing retainer plans after that for maintenance, monitoring and feature iteration.
Yes — most AI & Automation engagements involve integrating with or migrating from systems you already run. We audit your current setup during discovery before proposing an approach.
We run weekly demos and keep a shared channel open throughout, but we don't need daily involvement — a single point of contact for decisions is usually enough.
Scope changes are normal. We track them against the original proposal and give you a clear cost/timeline impact before any change is built, so there are no surprises on the invoice.