01
Discovery sprint
A short, focused engagement to find where AI pays for itself in your business and plan the first build.
- Opportunity map ranked by return
- Technical plan and architecture
- Scoped, fixed quote for the first release
KryloqWhat we build
AI systems that do real work, every day.
We build three kinds of systems. Each starts from a business outcome, runs on your real data, and ships with the evaluation, monitoring and documentation that production needs.
001agent.runtime
We build your AI agents
Support agents that resolve tickets end to end, internal assistants that know every doc, deal and decision, and research agents that do the legwork before your team does. Each one is connected to your systems, permission-aware, and measured against real conversations before it goes live.
What you get
- 01Agent design: tools, permissions, escalation paths
- 02Retrieval over your docs, tickets, CRM and databases
- 03Evaluation suite built from your real conversations
- 04Guardrails, audit logs and human-in-the-loop review
- 05Deployment into Slack, your app, email or helpdesk
Typical use cases
- Tier-1 customer support
- Company knowledge assistant
- Sales and account research
- Internal IT and HR helpdesk
Common stack
OpenAI / Anthropic / LangGraph / pgvector / Slack / Zendesk
agent.runtime6 tools
> where is order #4821, and can I change the delivery address?
- ✓search_orders(#4821)0.4s
- ✓check_policy(address_change)0.2s
- ✓update_shipping(new_address)0.6s
- reply drafted · 2 sources cited
ZendeskShopifySlackPostgres
evalspassing
002workflow.engine
We automate your operations
Most operational work is a chain of small judgements: read the email, work out what it is, update the CRM, tell the right person. We turn those chains into reliable pipelines, using deterministic steps where rules are enough and models only where judgement is needed.
What you get
- 01Process mapping and automation roadmap
- 02Document and email understanding pipelines
- 03CRM, ERP and spreadsheet integrations
- 04Exception queues with human review
- 05Monitoring, alerts and run history
Typical use cases
- Lead intake, scoring and routing
- Invoice and contract processing
- Reporting and CRM hygiene
- Meeting notes to tasks and follow-ups
Common stack
n8n / Temporal / Python / HubSpot / Salesforce / Google Workspace
workflow.engine4 steps · live
routed
auto
exceptions
to human
history
logged
003platform.build
We ship your AI product
We work as your product engineering team: scoping the smallest version worth shipping, building the AI core and everything around it (auth, billing, dashboards, APIs) and running it in production with the monitoring and cost controls a real product needs.
What you get
- 01Product scoping and technical architecture
- 02Full-stack web app with auth and billing
- 03AI features with evals and cost controls
- 04APIs, admin tools and analytics
- 05Infrastructure, CI/CD and observability
Typical use cases
- AI-native SaaS MVPs
- AI features inside an existing product
- Internal platforms and portals
- Customer-facing copilots
Common stack
Next.js / TypeScript / Postgres / Vercel / AWS / Stripe
platform.buildv1 → production
app.yourproduct.com
build
test
evals
deploy
004How we engage
Start small. Scale what works.
Every engagement starts with discovery, so you only commit to a build once you know what it will deliver.
02
Build
We design, build and launch one system end to end, against agreed success metrics.
- Weekly demos on your real data
- Evals and guardrails before launch
- Production deployment and handover docs
03
Embedded AI team
An ongoing team that keeps shipping, monitoring and improving your AI systems month after month.
- Roadmap owned together
- Monitoring, tuning and new capabilities
- Flexible capacity as you grow
→Next step
Not sure which one you need?
That's what the discovery call is for. Bring the problem; we'll tell you honestly whether AI is the right tool and what we'd build first.
Or see what we build