How fast can you start?

Discovery usually kicks off within one to two weeks of signing. If you have a hard deadline, tell us upfront — we'll be straight about whether we can hit it.

Do you work with early-stage startups?

Yes — about half our projects are pre-seed to Series A companies building their first real product. We scope tightly so you ship something meaningful, not a prototype that gets thrown away.

Can you sign an NDA before we share details?

Of course. We sign NDAs routinely before first calls — no awkwardness, no delay.

What does a first call look like?

30 minutes on your goals, constraints, and timeline. We'll ask sharp questions, tell you honestly if we're a fit, and outline what a scoped engagement could look like — no slide deck, no pressure.

How do you communicate during a project?

Weekly demos, async updates in Slack or your tool of choice, and a shared board so you always know what's in progress.

Yes — full ownership from day one. Repos, infrastructure, and docs are yours.

What happens after launch?

Every fixed-scope build includes 30 days of post-launch support. After that, most clients move to a retainer or embedded team.

Can you work with our in-house team?

Absolutely. We plug into your workflow, PR process, and standups — as extra senior capacity, not a separate silo.

How much does a typical project cost?

Fixed-scope builds typically start around $18k. Embedded teams run from $12k/month. We'll give you a clear number after discovery — not a range that doubles mid-project.

Do you work on hourly or fixed price?

Both, depending on the engagement. Fixed price for clearly scoped builds; monthly retainer or embedded team for ongoing work. We avoid open-ended hourly without a cap — that's how budgets spiral.

What if scope changes mid-project?

It happens. We document the change, agree on impact to timeline and budget before writing code, and keep a clear change log so nothing surprises you at invoice time.

What tech stack do you use?

React / Next.js, TypeScript, Node.js, and PostgreSQL for most web products. React Native or native Swift/Kotlin for apps. Python and modern LLM APIs for AI features.

Can you work with our existing codebase?

Yes. We start with a short technical audit, identify risks, and propose a pragmatic path forward.

How do you handle AI features responsibly?

We ground models on your data, evaluate outputs before launch, add guardrails for edge cases, and monitor usage and cost in production.

03 · AI & Automation

AI & Automation

Custom AI features and internal automations that save your team real hours every week — grounded in your own data, and evaluated before they ever ship.

40%+Typical ticket deflection

EvalsBefore every production ship

24/7Automated workflows

hostyler-ai

Summarize this week's tickets

3 recurring issues found. Drafting a fix doc now

40%+Typical ticket deflection
EvalsBefore every ship
Your dataNever trains public models
24/7Automated workflows

Overview

Intelligence that survives
real users.

Demos are easy. We scope, evaluate, and monitor every AI feature like any other part of your stack — so it still works when traffic spikes.

AI workflow integrated into a business tool

01 · For users

Answers they can trust

Grounded responses with citations — not confident hallucinations. High-stakes outputs go through human review.

02 · For your team

Hours back every week

Automations that replace repetitive ops work — with clear metrics on time saved, accuracy, and cost per task.

New AI features

Copilots, search, and generation built on your data — with RAG pipelines, evals, and guardrails before launch.

RAGEval suitesGuardrailsCost controls

Integrate into your product

Add AI to an existing app via your APIs and auth — feature-flagged rollout with monitoring from the first user.

Feature flagsYour VPCSlack / ZendeskDashboards

What we build

Four kinds of AI work
we productionize.

We start with one high-ROI workflow — then expand once quality and cost are proven in production.

Support & customer copilots

Draft replies from your help center, suggest macros, and route edge cases — with citations and human review for high-stakes answers.

Internal search & knowledge bases

RAG over wikis, contracts, and tickets so teams find answers in seconds — not by paging through Slack or SharePoint.

Ops & reporting automation

Scheduled pipelines that extract, summarize, and route data — replacing manual exports and spreadsheet gymnastics.

AI feature embedded in a software product

Product-embedded AI features

Search, recommendations, and generation inside your app — behind feature flags, with evals and cost controls from day one.

Fit & scope

Is this the
right fit?

If manual work is eating your team's week, there's probably an automation here.

ROI focusOps + Support

Great for

6 scenarios

Support teams drowning in repetitive tickets
Ops teams doing manual data entry or reporting
Products that want AI search or recommendations
Internal tools needing document or voice processing
Sales teams needing proposal and CRM automation
Companies with proprietary data that generic chatbots cannot use

What's included

Every engagement

LLM-powered chat, search & generation
Retrieval-augmented generation (RAG)
Internal workflow automation
Model evaluation & guardrails before launch
Usage monitoring & cost tracking
Ongoing tuning as usage grows
Prompt versioning & A/B testing
Human-in-the-loop review flows
Data pipeline & embedding infrastructure

How we think

Principles that shape
every build.

These are the defaults we bring unless your product needs something different.

Grounded, not generic

Every answer cites your data. Generic chatbot wrappers are demos — we build retrieval and guardrails around your content.

Evaluated before launch

Test suites built from your real tickets, docs, and edge cases. We catch regressions before users do.

Cost-aware by design

Caching, routing, and budgets are planned during prototyping — not discovered when the invoice arrives.

Observable in production

Latency, quality, spend, and failure modes are tracked in dashboards your team can actually use.

How we build

From scope to ship.

A clear, repeatable process for every ai & automation engagement.

01
Use-case & data audit
1–2 weeks
We identify high-ROI workflows, assess data quality and access, and define success metrics — time saved, resolution rate, accuracy, or cost per task.
02
Prototype & evaluate
2–4 weeks
A working prototype on a representative dataset, with eval suites that catch regressions before users do. We compare models and architectures on your actual content.
03
Production integration
3–6 weeks
APIs, UI, auth, and logging wired into your existing tools — Slack, Zendesk, your app, or internal dashboards. Guardrails and fallbacks are built in.
04
Monitor & improve
Ongoing
Dashboards for usage, latency, cost, and quality. We tune prompts, retrieval, and routing as real traffic patterns emerge.

What you get

What we use, and what you get.

The tools and outputs for a typical ai & automation project with Hostyler.

Technologies

Chosen for your product

Battle-tested tools — not whatever is trending this week.

OpenAIAnthropicLangChainPineconePostgreSQL pgvectorPythonNode.jsTemporal

Deliverables

What you walk away with

Everything needed to launch, maintain, and grow.

Production AI feature or automation
Eval suite & quality benchmarks
RAG pipeline & vector index
Admin tools for prompt & content management
Cost & usage monitoring dashboard
Runbook for model updates
Security review for data handling

Why it matters

Why AI and automation belong in your business

The right AI features save hours every week, speed up decisions, and improve customer experience — when they are grounded in your data and built for production, not demos.

Reduce repetitive work

Automate ticket triage, data entry, report generation, and document review so your team focuses on work that needs human judgment.

Faster answers for customers

Support copilots and knowledge search help agents and customers find accurate answers in seconds — with citations from your own content.

Smarter product experiences

Add search, recommendations, summarisation, and generation inside your app — features users expect from modern software.

Measurable ROI

We define success metrics upfront — time saved, deflection rate, accuracy, and cost per task — so you know whether the investment pays off.

What we can build

Examples for your business.

Practical use cases we deliver — from customer-facing apps to internal automations.

Support copilots

Draft replies from your help centre, suggest macros, and route complex cases — with human review for high-stakes answers.

Internal knowledge search

Search across wikis, contracts, SOPs, and tickets so teams stop digging through Slack threads and shared drives.

Sales & proposal automation

Generate first drafts of proposals, summarise CRM notes, and pull relevant case studies for outbound teams.

Ops & reporting pipelines

Scheduled workflows that extract, summarise, classify, and route data — replacing manual exports and spreadsheet work.

Document processing

Extract fields from invoices, contracts, and forms; flag anomalies; and push structured data into your systems.

Product-embedded AI

Add AI search, assistants, and generation inside your SaaS — behind feature flags with evals and cost controls from day one.

Working together

How AI engagements usually start

We begin with a use-case and data audit — then a prototype with evals before anything touches production traffic.

01
Discovery call to map workflows, data sources, and success metrics
02
Data access review and ROI estimate (time saved, deflection, accuracy)
03
Prototype on a representative dataset with eval suite
04
Production integration behind feature flags with rollback plan
05
Monitoring dashboard and tuning window after launch

How do you prevent AI hallucinations?

We use retrieval grounding, citation requirements, confidence thresholds, and human review for high-stakes outputs. Every feature ships with an eval set built from your real data.

Will our data be used to train public models?

No. We use enterprise API terms and can deploy on your VPC or preferred cloud when data residency matters.

What does AI cost at scale?

We model cost per task during prototyping and set budgets, caching, and routing rules before launch. You get alerts when spend deviates from plan.

Can you add AI to our existing product?

Yes — that is the most common engagement. We integrate via your APIs and auth, ship behind feature flags, and roll out gradually.

How long until we see ROI?

A focused copilot or automation often reaches production in 6–10 weeks. We define success metrics in week one so you know if it is working.

What do you need from us to start?

Sample data (tickets, docs, or reports), access to relevant systems, a product owner for feedback, and clarity on what success looks like — time saved, deflection rate, or accuracy.

Explore our other disciplines.

Ready when you are.

If this sounds like the right fit, tell us what you're building — we'll reply within one business day with next steps, not a sales pitch.

Start an AI project Or email us directly