AI Development Services

Build powerful AI applications with our expert team. Custom chatbots, workflow automation, API integrations, and intelligent solutions for your business.

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What is AI development for a service business?

AI development is the work of plugging a language model (or several) into your existing tools so it handles a specific job — answering customer questions, drafting estimates, qualifying leads, summarizing call notes — at a fraction of the time and cost. It is not a science project. It is a focused integration with measurable output.

For service businesses, this usually shows up as a 24/7 intake assistant on your website, a back-office tool that turns raw call transcripts into CRM records, or a search interface that lets your team query years of project files in plain English. The model is the easy part. The real work is connecting it to your data, defining what "good" looks like, and shipping something your team will actually use.

Verlua builds AI features on top of standard web stacks (Next.js, Node, Python where it earns its keep) and the major model providers — OpenAI, Anthropic, and open-weight models when self-hosting is required. We focus on the small set of integrations that move revenue or reclaim hours, not on flashy demos that never make it past pilot.

Prompt-only vs RAG vs fine-tuned: which approach fits?

Most AI projects land in one of three buckets. Picking the right one up front saves months of rework. Here is how the three approaches compare on cost, timeline, and what they are good for.

Approach	Typical cost	Timeline	Best for
Prompt + API	$8K-$20K	2-4 weeks	Chatbots, intake forms, summarization, content generation
RAG (retrieval-augmented)	$20K-$60K	6-10 weeks	Answering from your knowledge base, policy lookup, document Q&A
Fine-tuned / multi-agent	$60K+	3-6 months	High-volume workflows, domain-specific tone, custom reasoning chains

Ranges reflect Verlua engagements; final scope depends on data readiness, integrations, and compliance requirements.

What is included in a Verlua AI engagement?

Every project ships with the same core set of deliverables, regardless of which approach we pick. You own the code, the prompts, the data pipelines, and the documentation.

Custom AI Applications

Intake assistants, internal copilots, document Q&A tools, and workflow automations built around your specific processes.

Chatbots & Voice Agents

Conversational interfaces that qualify leads, book appointments, and handle Tier-1 support without dropping into canned-response mode.

Workflow Automation

Background jobs that read inbound email, transcribe calls, draft proposals, and push the results into your CRM or job-management tool.

API & CRM Integrations

Connections to HubSpot, Salesforce, ServiceTitan, Jobber, Airtable, and any REST/GraphQL endpoint your stack relies on.

Evaluation & Guardrails

Test suites that catch regressions when models update, plus content filters and PII scrubbing to keep sensitive data out of prompts.

Ongoing Support

Prompt tuning, model upgrades, and feature additions on a monthly retainer so the system gets better, not stale.

Which AI use cases actually move revenue for service businesses?

The features that earn their keep are boring on paper and dramatic in practice. Across our client base, four patterns show up again and again:

Lead intake and qualification

A chatbot on your contact and service pages that asks the right qualifying questions, captures answers in your CRM, and books a call only with leads that match your fit criteria. Cuts the time front-desk staff spend on tire-kickers.

Estimate and proposal drafting

Feed in a transcript or notes from a site visit, get a first-draft proposal with line items, scope, and timeline. The estimator edits instead of writing from scratch.

Internal knowledge search

A search box across years of past projects, SOPs, training docs, and vendor specs — anyone on the team can ask "what did we charge the Andersons for the kitchen?" and get a sourced answer in seconds.

Review and follow-up automation

Personalized review requests and follow-up emails generated from job notes, not boilerplate. Higher response rates, more reviews, less work for the office.

How does Verlua's AI development process work?

We move from idea to live system in four stages. Every stage ends with something you can see and decide on, not a status update.

Discovery

One to two weeks. We map the workflow, audit your data, benchmark 2-3 candidate models against real samples, and agree on the success metric. You get a fixed-price build proposal at the end.

Proof of Concept

Two to three weeks. We build the smallest version that proves the system can hit your target metric on a sample of real data. If it cannot, we stop here and refund the build phase.

Production Build

Four to twelve weeks depending on scope. We wrap the POC in the integrations, auth, logging, eval suite, and UI it needs to run in front of customers or staff every day.

Operate & Improve

Ongoing. Monthly retainer covers monitoring, prompt tuning, model upgrades when better/cheaper options ship, and adding the next feature on your roadmap.

What does a typical Verlua AI stack look like?

We default to boring, well-supported tools so your system keeps working in three years. Most projects use some subset of the stack below, with substitutions when compliance or hosting constraints require them.

Models

OpenAI GPT-4 class (default for quality)
Anthropic Claude Sonnet / Opus
Llama 3 / Mistral (self-hosted)
OpenAI Whisper (transcription)

Application

Next.js + TypeScript
Python (FastAPI) for ML-heavy services
Vercel AI SDK / LangChain where useful
Postgres + pgvector for RAG

Infrastructure

Vercel, AWS, or your existing cloud
Background jobs (Inngest, Trigger.dev)
Observability (Langfuse, Helicone)
SSO + role-based access

Evaluation

Golden-set regression tests
Human-in-the-loop review queue
Cost and latency dashboards
PII filtering before model calls

Frequently asked questions about AI development

How much does an AI development project cost?

Most engagements fall into three bands. A focused prompt-and-API build (intake forms, chatbots, summarization) typically runs $8K-$20K. A retrieval-augmented (RAG) system with a custom knowledge base runs $20K-$60K. A multi-agent or fine-tuned production system starts around $60K and scales with data and integrations. We scope a fixed price after a paid discovery sprint.

How long does it take to build an AI solution?

Simple chatbots and intake automations ship in 2-4 weeks. RAG systems with custom data take 6-10 weeks. Production multi-agent or fine-tuned workflows take 3-6 months. We always start with a 1-2 week proof of concept so you see real output before committing to the full build.

Which AI model should my business use?

For most service-business use cases, hosted models from OpenAI (GPT-4 class) or Anthropic (Claude Sonnet/Opus) win on quality-to-cost. Open-weight models (Llama, Mistral) make sense when data cannot leave your servers or when call volume is very high. We benchmark 2-3 candidates against your real data during discovery and pick on accuracy, latency, and per-call cost.

Will my customer data be safe?

Yes. We use enterprise endpoints (OpenAI, Anthropic, Azure OpenAI) that contractually do not train on your data, and we can deploy entirely on your infrastructure when regulations require it. PII is filtered before it reaches the model, prompts and outputs are logged for audit, and access is role-gated.

How do you measure AI project ROI?

Before we build, we agree on one or two business metrics — minutes saved per ticket, conversion rate on intake forms, leads qualified per week, support tickets deflected. Every deployment ships with a dashboard tracking those numbers against a pre-AI baseline so you can see what the system is actually doing.

Do you provide ongoing support for AI systems?

Yes. AI systems drift as models update and user behavior shifts, so monthly support covers prompt tuning, model upgrades, regression testing, and adding new capabilities. Most clients keep us on a retainer for the first 6-12 months after launch.

Ready to scope an AI project?

Book a strategy call. We will walk through your workflow, identify the highest-leverage place to start, and give you a realistic price and timeline.