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Build applications around language models with clear operating rules.
Connect language models to your interface, data and business logic. Evaluate model behavior against your examples and choose an integration approach that fits your operating constraints.
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Overview
LLM Application Development, explained
Large language model applications go beyond a chat box. They combine a model with your data, business rules and user interface so people can ask, draft, classify or analyse in the context of their work. The engineering challenge is reliability: consistent outputs, sensible costs and behaviour you can test.
We design LLM applications as normal software with an AI component. That means structured prompts, typed outputs, retrieval where needed, evaluation suites, observability and a release process. You get an application that can be maintained and improved like the rest of your product.
Before you build
Decisions worth settling first
Settling these points early keeps scope realistic and makes the estimate meaningful.
01
User and task
Identify who uses the feature and the specific job it helps them complete.
02
Context needed
Decide which records, documents or history the model needs and how to retrieve them safely.
03
Output format
Define whether results are free text, structured fields or actions, and how they are validated.
04
Evaluation set
Gather representative inputs and expected results to compare prompts and models objectively.
05
Model and hosting
Choose between hosted APIs and self-hosted models based on privacy, cost and latency.
06
Observability
Plan logging of prompts, responses, cost and user feedback from the first release.
Services
What our LLM application development services include
LLM prototyping
Fast prototypes on real examples to confirm value before committing to a full build.
Production LLM features
Robust implementation with retries, timeouts, caching, typed outputs and fallbacks.
RAG and context engineering
Retrieval pipelines that give the model the right context with citations.
Fine-tuning and adaptation
Fine-tuning or prompt optimisation where a base model is not consistent enough.
Model evaluation
Comparison of models and prompts against your evaluation set on quality, cost and speed.
LLMOps
Monitoring, prompt versioning, cost dashboards and regression testing in production.
How it is built
The layers behind a dependable result
Every layer has an owner, tests and documentation, so the system can be supported after launch.
01
Input handling
Validation, redaction of sensitive data and routing to the right prompt or model.
02
Context assembly
Retrieval and formatting of documents, records and conversation history.
03
Model call
Prompting, structured output, retries and fallback models.
04
Post-processing
Validation, citation checks, business rules and logging before results reach users.
Use cases
Where it helps
In-app assistants
Help users configure, search or analyse within your product.
Classification and routing
Categorise tickets, emails and requests and send them to the right queue.
Data extraction
Pull fields from unstructured text into your database.
Analysis and Q&A
Answer questions over reports, contracts or logs with references.
Translation and localisation
Translate content with glossary control and human review.
Developer tools
Generate code suggestions, test data or documentation inside internal tools.
Illustrative use case
An application can turn a user request into a structured draft that is checked before the next system action.
Why PoliteTech
Why teams choose PoliteTech
Scope before commitment
Priorities, assumptions and acceptance criteria are agreed in writing before a delivery plan is fixed.
Engineers who build
Recommendations come from the people who design, code and support the software, so they reflect real implementation effort.
Visible progress
A shared backlog, regular demos and recorded decisions keep you informed without chasing updates.
Practical AI
Evaluation sets, review steps, cost limits and monitoring are part of every AI feature we ship.
Flexible engagement
Start with a scoped project, a dedicated team or support, and change the model as your roadmap evolves.
Clear ownership
Repositories and accounts can live in your organisation, with IP and confidentiality terms agreed in the contract.
Industries
Where this work fits
Retail and Ecommerce
Build web and mobile journeys around product discovery, ordering and customer service.
Financial Software
Support reporting, document processing and internal operations through defined application workflows.
Logistics Software
Connect planning, task assignment and operational updates in one workflow.
Education and Elearning
Create platforms for content, activities and progress tracking.
Fitness and Wellness Apps
Support tracking, coaching content and user engagement around a clear product goal.
Manufacturing Software
Replace fragmented tracking with workflows for quotations, work status and supporting documents.
Real Estate Applications
Create listing, enquiry and internal coordination tools around your business process.
Business Operations
Build task, approval and reporting tools around the way your team works.
Process
A practical path from requirements to release.
Each stage ends with something you can review: a scope, a prototype, a working increment or a release.
01
Understand
Discuss your users, business needs, existing systems and delivery constraints.
02
Define
Agree scope, technical approach, responsibilities and a delivery plan.
03
Design and build
Develop in manageable increments with visible progress and review points.
04
Test and release
Validate agreed workflows, resolve release blockers and prepare deployment.
05
Support and improve
Document the handover, review production feedback and plan the next priorities.
Technology
Tools and platforms we work with
OpenAIGoogle GeminiAnthropic ClaudeLlamaMistralHugging FacevLLMLangChainLlamaIndexPythonTypeScriptFastAPIpgvectorRedisAWSAzureGoogle Cloud
Cost
What shapes the cost of your project?
There is no useful price without scope. These are the factors we review when preparing an estimate, and the estimate lists every assumption so you can see what would change it.
01
Number of workflows
Each workflow needs its own prompts, tools, evaluation set and review design.
02
Data preparation
Cleaning, labelling, chunking and access rules often take more effort than the model itself.
03
Integrations
Connections to CRMs, ERPs, document stores and identity providers add design and testing work.
04
Evaluation and review
Building test sets, review queues and monitoring is essential for reliable results.
05
Model and hosting
Hosted APIs, open models and private deployment have very different setup and running costs.
06
Security and compliance
Sensitive data, audit needs and data residency add controls and documentation.
Engagement models
Choose the engagement that fits your next step.
Dedicated Team
For a changing roadmap or ongoing development. Agree roles and working process, then manage priorities through a shared backlog.
Best for team extension and long-term product work.
Scoped Project
For a defined product or milestone. Align on requirements, deliverables and acceptance criteria, with changes reviewed before they affect the plan.
Best for MVPs, integrations and focused releases.
Maintenance and Improvement
For software that already exists. Start with a technical review, then agree support routines and planned improvements.
Best for live applications and modernisation.
FAQ
Questions about LLM application development
Should we use a hosted or open model?
Hosted models are fastest to start and strong on quality. Open models give more control over data and cost at scale. We compare both on your examples.
Is fine-tuning necessary?
Usually not at first. Good prompts, examples and retrieval solve most problems. Fine-tuning helps when you need consistent format or domain behaviour at volume.
How do you handle sensitive data?
We minimise what is sent, redact where possible, configure providers not to retain data and use private deployment when policy requires.
How do you keep outputs consistent?
Structured outputs, validation, low-variance settings and an evaluation suite that runs whenever prompts or models change.
How fast will it respond?
Latency depends on model and context size. We design streaming responses, caching and smaller models for simple steps.
Can you improve an LLM feature another team built?
Yes. We start with an evaluation of current quality, cost and failure cases, then prioritise fixes.
Can we begin with a focused scope?
Yes. We can review your current situation and agree a bounded first engagement before a wider build.
How are pricing and timelines determined?
We review requirements, technical complexity, team needs and delivery constraints before proposing an engagement. Estimates list assumptions and what may change the plan.
Can we discuss a project under an NDA?
Yes. Let us know before sharing confidential materials so we can agree confidentiality terms first.
Our work
LLM-powered products we have built
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AI Study Companion
A mobile AI tutor that helps children aged 6 to 18 with homework, exam preparation and reading, explaining step by step instead of giving answers.
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Discuss your LLM application development needs
Share your goals, current situation and the help you need. We will review your message and suggest a practical next step.