Since 2010
Building Software
Applications, Not Experiments.
LLM Development Services
We engineer production systems on large language models: retrieval pipelines, fine-tuned adapters, cost-optimised serving, and evaluation that keeps quality measurable. Fixed pricing, senior AI engineers, full source-code ownership.
- 120+
- AI Engineers
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 25+
- Countries Served
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
What LLM development actually involves
The model is a small part of the system. Real LLM engineering is the plumbing around it: ingestion and chunking, retrieval quality, prompt and context management, structured output, latency and cost engineering, and an evaluation loop that runs on every change. Teams that skip those layers ship demos that collapse under real traffic.
We build that full stack, model-agnostic, so a model swap is configuration rather than a rewrite. When the use case calls for autonomy rather than answers, our AI agent development team extends the same foundations.
What our LLM development services cover
The engineering layers between a model and a product.
LLM application engineering
Product features powered by LLMs with streaming UX, structured outputs, and failure handling designed for real traffic.
- ✓Streaming responses
- ✓Structured output schemas
- ✓Graceful degradation
- ✓Rate and quota handling
RAG pipeline engineering
Retrieval-augmented generation tuned to your corpus: chunking, embeddings, hybrid search, and reranking measured on real queries.
- ✓Corpus ingestion
- ✓Hybrid search
- ✓Reranking
- ✓Query analytics
Fine-tuning and distillation
Adapters and distilled models that cut cost and latency on narrow tasks, with evaluation proving the trade is worth it.
- ✓Dataset curation
- ✓LoRA adapters
- ✓Distillation pipelines
- ✓Regression evals
Serving and cost optimisation
Caching, batching, routing, and quantisation strategies that keep unit economics viable as volume grows.
- ✓Semantic caching
- ✓Batch and streaming serving
- ✓Model routing
- ✓Token budget controls
Evaluation and monitoring
Automated test suites, LLM-as-judge scoring, and production monitoring so quality is a tracked number per release.
- ✓Golden datasets
- ✓Regression gates in CI
- ✓Drift alerts
- ✓Cost and quality dashboards
Self-hosted and private lLMs
Open-weight models served in your own environment where data residency or cost demands it.
- ✓Open-weight deployment
- ✓GPU sizing
- ✓Quantisation
- ✓Private inference endpoints
What every engagement includes
Every LLM 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 LLM 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 LLM 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 on lLMs
Application patterns we deliver most often.
Enterprise search and Q&A
Natural-language question answering over your documents, with citations and access control respected per user.
Document intelligence
Extraction and classification of unstructured documents into structured data your systems can act on.
Domain assistants
Specialised copilots for legal, medical, finance, or engineering workflows, grounded in domain content.
Content and code tooling
Drafting, summarisation, and transformation features embedded in your product with review controls.
Private LLM platforms
Self-hosted inference for regulated workloads, with serving and monitoring handled end to end.
How a LLM Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your LLM 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 LLM 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. LLM 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 LLM 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 LLM builds
Default choices, stated with reasons.
Models
Frontier APIs where capability matters; open-weight models self-hosted where residency, cost, or latency demands it. Swappable by design.
Retrieval
Vector databases with hybrid keyword search and reranking, chunking strategies tuned per corpus and validated on real queries.
Serving
Managed inference with semantic caching and model routing; GPU-backed self-hosting with quantisation where economics favour it.
Quality
Evaluation suites in CI, LLM-as-judge scoring, and production trace monitoring, so regressions surface before users find them.
How intelligence flows through the system
What the model reads, and what it is allowed to change.
Context engine
Builds prompts from retrieval, user context, and conversation state.
Reads
Knowledge base, user profile, history
Changes
Nothing; context is read-only
Inference router
Chooses the model per task on cost, latency, and capability.
Reads
Task type, budgets, model telemetry
Changes
Routing tables and cache entries
Quality gate
Validates outputs against schemas and evaluation thresholds.
Reads
Responses, eval suites, user feedback
Changes
Approval queues and quality metrics
What's included, standard
The baseline on every LLM 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 LLM 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.
Model and cost strategy session
A working session on model selection, serving options, and unit economics for your workload.
API models vs Self-hosted lLMs
The honest trade-offs, as we advise them at discovery.
| Criteria | Consideration | Managed API Models | Self-Hosted Open Weights |
|---|---|---|---|
| Capability ceiling | Highest, always current | Trails frontier by months | |
| Data residency | Vendor's cloud, contractual controls | Fully inside your environment | |
| Cost at scale | Per-token, predictable at low volume | Fixed GPU cost, cheaper at high volume | |
| Ops burden | None, vendor-managed | Your team or ours runs serving | |
| Time to launch | Days | Weeks, including GPU setup |
/11
Why teams pick us for LLM work
The differences live in engineering habits, not pitch decks.
Model-agnostic architecture
Tools, evaluation, and retrieval are built model-neutral, so a model swap or a price change is configuration, not a rebuild.
Unit economics first
We engineer caching, routing, and model mix against cost-per-output targets, so the system survives scale commercially.
Evaluation in the pipeline
Golden datasets and regression gates run in CI, so quality is measured per release instead of argued about after incidents.
120+ AI engineers
Retrieval tuning, fine-tuning, and serving optimisation are senior specialisms on our bench, applied full-time.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with production LLM systems among them.
LLM development FAQs
$ moxace launch --nowReady to Build on LLMs Properly?
Talk to our LLM engineering team about a fixed-scope build with evaluation, cost modelling, 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
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