AI Solutions
Applied AI with a business case, not a science project.
AI & Automation
Generative AI wired into how you work
The value of generative AI is rarely the model. It is the plumbing around it — your data, your rules, your review process, your systems — that turns a clever demo into daily leverage.
- Engagement
- Fixed-scope build or product retainer
- Typical timeline
- 6–12 weeks
- Delivered across
- All 50 US states
Overview
Anyone can put a prompt box on a website. What is hard is a generative system that reliably produces work your team would sign their name to: proposals that use real pricing, reports that cite actual figures, summaries that never omit the material clause, marketing copy that respects brand and legal constraints.
That reliability comes from architecture. Retrieval over your authoritative sources. Structured output validated against a schema. Deterministic checks on anything numerical. Templates that constrain format. Human review positioned exactly where the risk sits. We build all of it, then measure output quality against a rubric so improvement is verifiable.
We also handle the parts that decide whether people actually use it: putting generation inside the tools they already work in, making revision fast, and keeping latency low enough that the assistant feels like help rather than a wait.
- Faster first drafts on structured documents
- 5–10×Faster first drafts on structured documents
- Validated structured output, not free text
- SchemaValidated structured output, not free text
- Quality scored against a defined rubric
- WeeklyQuality scored against a defined rubric
Business benefits
What generative ai development changes for you
The reasons this work earns its budget, stated as outcomes rather than features.
Grounded in your sources
Retrieval over your documents, data and prior work so output reflects your actual facts, pricing and precedent.
Structured, validated output
Generation into a schema with automatic validation, so downstream systems can consume results without a human retyping them.
Brand and compliance constraints
Style rules, banned claims, required disclosures and legal language enforced in the generation pipeline rather than caught in review.
Inside the tools people use
Delivered in your CMS, CRM, document system or internal app — adoption collapses when it lives in a separate tab.
Quality measured, not assumed
Automated scoring against a rubric, plus human spot-checks, so prompt and model changes are validated before rollout.
Cost engineered down
Caching, model routing and prompt efficiency so unit cost falls as usage rises instead of scaling linearly.
Problems solved
If any of this sounds familiar
These are the situations clients describe in the first conversation, and what we do about each.
“Generic AI output needs so much editing it saves nothing.”
Ground generation in your own material and constrain it with templates and style rules. The difference between generic and useful is context, supplied deliberately.
“Producing proposals and reports consumes senior time.”
A generation system that assembles documents from your real data and prior work, leaving experts to review and refine rather than start blank.
“Marketing cannot produce enough content for the SEO plan.”
An assisted pipeline — brief, outline, draft, human edit, publish — that multiplies output while keeping a person accountable for what ships.
“Legal will not approve AI-generated customer-facing content.”
Enforced disclosure language, banned-claim filters, citation requirements and a mandatory approval step, with a full audit trail of what was generated and who approved it.
Our process
How we deliver it
Each stage has a defined output, so you always know what you are getting and when.
- 01
Output definition
Define precisely what good looks like, with examples and a rubric, before building anything. Vague targets produce vague systems.
- 02
Context engineering
Identify and prepare the sources the system must draw on, and design the retrieval strategy that surfaces the right ones.
- 03
Pipeline build
Prompt architecture, structured output, validation, guardrails and integration into the tool where the work happens.
- 04
Evaluation
Score generated output against the rubric, tune, and establish the regression suite that protects quality over time.
- 05
Rollout
Train the team on effective use and honest limitations, then monitor adoption, quality and cost together.
Technologies used
The tools behind the work
Chosen for maintainability and hiring depth rather than novelty. We will justify any choice on request, and we avoid technology that makes you dependent on us.
- Claude
- OpenAI
- Azure OpenAI
- AWS Bedrock
- LangChain
- pgvector
- Zod
- Python
- TypeScript
- LangSmith
Industries served
Where this work lands most often
Sector context changes what good looks like. These are the industries where we have delivered this service repeatedly.
Why Mova
What working with us on generative ai development is like
The same commitments apply to every engagement, regardless of size or service.
A defined first step
We scope a fixed-price starting point so you can evaluate us on generative ai development before committing to a program.
Senior people, named
The team you meet is the team that delivers. You will know exactly who is accountable.
You own the output
Code, accounts, files and documentation are yours from day one, with no lock-in of any kind.
Measured, then reported
We baseline before starting and report against it honestly — including the months that fall short.
Questions
Generative AI Development: common questions
The questions we are asked most about generative ai development, answered directly.
Whichever performs best for the task at an acceptable cost — typically Claude or GPT-class models for reasoning-heavy work, smaller and cheaper models for classification and routing. We build an abstraction layer so the choice stays reversible.
Keep exploring
Related services
AI Agents
Systems that complete work, not just answer questions.
Content Marketing
Publishing that builds demand instead of filling a calendar.
Machine Learning Solutions
Prediction, classification and forecasting on your own data.
Copywriting
Words that carry their weight on the page and in the funnel.
Next step
Ready to talk about generative ai development?
Thirty minutes with someone who has delivered this work. We will tell you what it would take, roughly what it would cost, and whether Mova is the right fit.
No pitch deck. A 30-minute conversation about what you are trying to achieve.