Since 2010
Building Software
Generation With Guardrails.
Generative AI Development Services Company
We build generative AI systems that produce drafts, documents, images, and code your teams actually trust, with evaluation pipelines and guardrails measured per release. Fixed pricing, senior engineers, full source-code ownership.
- 120+
- AI Engineers
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 3,900+
- Apps Published
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
Where generative AI pays for itself
Generation is only valuable when it is controllable. The engagements that succeed share a shape: a narrow, high-volume task, a grounding layer over your real content, and an evaluation loop that catches quality drift before users do. First drafts of contracts, product descriptions, marketing variants, support replies, and report summaries are where the arithmetic works.
We build the full system, not just the prompt: ingestion, retrieval, generation, review UX, and telemetry. For conversational surfaces on top of the same stack, see our AI development services.
What our generative AI development services cover
From a first generation pipeline to an enterprise content platform.
Document and report generation
Structured drafting of contracts, summaries, and reports from your templates and data, with human review built into the flow.
- ✓Template-aware generation
- ✓Data grounding
- ✓Review and approve flows
- ✓Version and audit trails
RAG content systems
Generation grounded in your knowledge base so outputs cite evidence rather than invent facts.
- ✓Corpus ingestion
- ✓Hybrid retrieval
- ✓Citation rendering
- ✓Freshness automation
Fine-tuning and adapters
Model adaptation on your domain data where prompting plateaus, with evaluation proving the gain before you commit.
- ✓Dataset curation
- ✓LoRA and adapter tuning
- ✓Before/after evals
- ✓Serving cost modelling
Structured output pipelines
Generation that emits valid JSON, tables, and schemas your downstream systems can consume without string surgery.
- ✓Schema-constrained decoding
- ✓Validation and repair
- ✓Retry strategies
- ✓Type-safe consumers
Content review and guardrails
Brand, legal, and safety checks applied automatically before anything generated reaches production.
- ✓Policy classifiers
- ✓Banned-claims checks
- ✓Human approval gates
- ✓Output logging
Generative UX design
Interfaces that make generation editable: diffs, side-by-side variants, and one-click accept so users stay in control.
- ✓Inline editing
- ✓Variant comparison
- ✓Regeneration controls
- ✓Feedback capture
What every engagement includes
Every generative AI project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped generative AI roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All generative AI source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with generative AI
Systems our clients run in production, each with measurable throughput gains.
Contract drafting assistant
First drafts and clause suggestions generated from your precedents, routed to lawyers for approval rather than replacement.
Product content engine
Descriptions, translations, and SEO variants generated at catalogue scale with brand checks applied automatically.
Support reply drafting
Suggested replies for support agents grounded in your policies, cutting handle time while keeping humans in the loop.
Report summarisation
Long documents condensed into structured summaries with citations, ready for analyst review in minutes.
Marketing variant factory
Campaign copy and creative briefs generated per segment, with approval workflows before anything is published.
How a generative AI Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your generation goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The generative AI architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. generative AI work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged generation launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our generative builds
Default choices, stated with reasons.
Models
Frontier APIs for open-ended generation, fine-tuned or distilled models for high-volume, narrow tasks where cost per output matters.
Grounding
Retrieval over your corpus with metadata filters and freshness automation, so outputs reflect current reality rather than training data.
Structured output
Schema-constrained decoding and validation layers so generated content lands as usable data, not prose to be parsed by hand.
Quality control
Classifier and LLM-as-judge pipelines plus sampled human review, with quality scores tracked per release.
How intelligence flows through generation
What the model reads, and what it is allowed to change.
Context builder
Assembles templates, data, and retrieved passages for each request.
Reads
Templates, knowledge base, structured data
Changes
Nothing; context assembly is read-only
Generator
Produces drafts constrained to your schemas and style rules.
Reads
Assembled context and style guides
Changes
Draft documents and output records
Reviewer pipeline
Scores outputs against policy and quality checks before release.
Reads
Generated drafts, policy rules, eval suites
Changes
Approval queue and quality metrics
What's included, standard
The baseline on every generative AI engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each generative AI release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Evaluation design workshop
A working session defining quality criteria and test sets for your generation task, before build begins.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for generative AI
The differences live in engineering habits, not pitch decks.
Evaluation before embellishment
We define what good output means with test sets and scoring first, then optimise generation against them, so quality claims are checkable.
Cost per output modelled
Token economics are engineered upfront: which steps need frontier models and which run cheap, so unit costs survive scale.
Humans stay in the loop
Review UX is a designed feature, with diffs, approvals, and feedback capture that also improves the system over time.
120+ AI engineers
Fine-tuning, retrieval, and evaluation are senior specialisms on our bench, not an outsourced afterthought.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with production generative systems among them.
Generative AI development FAQs
$ moxace launch --nowReady to Generate at Production Quality?
Talk to our generative AI team about a fixed-scope build with evaluation, guardrails, and full source-code ownership.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
All product names, trademarks, and brands referenced are the property of their respective owners, used for identification purposes only. Read the full legal notice on our disclaimer page.