Generative AI development
Generative AI your business can put its name to.
Agleam Labs builds generative AI applications, copilots, and content tools that produce useful drafts, summaries, and answers inside the software your people already use. Every build includes guardrails, testing, and cost controls, so output is consistent and safe to rely on.
- Copilots and assistants
- Content and document generation
- LLM integration into existing apps
- Guardrails and evaluation built in
What we do
Large language models, made fit for business use.
Generative AI is good at producing language and media: drafting emails and reports, rewriting content for different audiences, summarising long material, and turning rough notes into structured output. We build applications that harness that ability for specific jobs, with prompts, templates, and interfaces designed around how your team works.
The hard part is making output dependable. We add input and output guardrails, brand and tone rules, structured formats, and automated evaluation so quality does not drift. When answers must come from your own documents, we pair generation with retrieval; when the system needs to take action, we extend it into an agent.
What we deliver
Generative AI applications, built properly.
From a copilot inside an existing product to a standalone content platform, we cover design, integration, and safe operation.
01 Copilots for internal teams +
- Sales copilots
- Support copilots
- Analyst assistants
- In-app AI
Assistants embedded in the tools staff use daily, helping them draft, review, summarise, and find the next step without leaving the screen.
02 Content generation tools +
- Templates
- Brand voice
- Approval flow
- Bulk generation
Platforms that produce product descriptions, marketing copy, proposals, and reports from your data and templates, with review and approval before anything is published.
03 LLM integration +
- API integration
- Streaming
- Fallbacks
- Multi-provider
Adding generative features to existing web and mobile applications through clean APIs, with streaming responses, retries, and fallbacks between model providers.
04 Guardrails and safety +
- Input filtering
- PII redaction
- Output validation
- Policy rules
Filters for harmful or off-topic input, checks on output format and facts, PII redaction, and refusal handling, configured to your policies.
05 Prompt engineering and structured output +
- Prompt versioning
- JSON schema
- Few-shot
- Templates
Versioned prompts, few-shot examples, and schema-validated JSON output, so generated content plugs straight into your systems without manual clean-up.
06 Fine-tuning and model adaptation +
- Fine-tuning
- LoRA
- Domain adaptation
- Benchmarking
Where prompting is not enough, we adapt models to your domain language or style using your examples, and compare the result against a strong baseline.
07 Image and multimodal generation +
- Image generation
- Vision models
- Multimodal
- Asset pipelines
Generating and editing images, reading charts and screenshots, and combining text with visual input for design, catalogue, and documentation tasks.
Why generative AI
Faster first drafts. Consistent quality.
-
Drafts in seconds
Writing that used to take an hour starts from a solid first draft.
-
Consistent voice
Templates and style rules keep every output on brand and on message.
-
Content at scale
Generate thousands of descriptions, summaries, or variants without extra headcount.
-
Safe by design
Guardrails and review steps stop inappropriate or inaccurate output reaching customers.
-
Predictable cost
Caching, model routing, and usage limits keep running costs under control.
-
Have a generative AI use case in mind? Book a free consultation and we will help you shape it.
Book a free consultation →
Why Agleam Labs
Generative AI without the guesswork.
Hover or tap a card to see what it means in practice.
How we work
From prompt prototype to production feature.
-
01
Shape the use case
We define the task, the users, what good output looks like, and the risks to guard against.
-
02
Prototype and evaluate
We test models and prompts against real examples and agree quality thresholds with you.
-
03
Build and integrate
We build the application or feature, add guardrails and monitoring, and connect it to your systems.
-
04
Release and tune
We launch to a pilot group, review output and feedback, and refine before wider rollout.
Agleam Labs in numbers
- Enterprise apps delivered
- 50+
- Enterprise apps delivered
- On-time delivery
- 99.8%
- On-time delivery
- Years of senior experience
- 10–30
- Years of senior experience
- Years of DevOps experience
- 20+
- Years of DevOps experience
- Technologies in production
- 20+
- Technologies in production
- Office, worldwide clients
- 1
- Office, worldwide clients
Technology
Models and tools we build with.
We work with leading hosted models and open-weight alternatives, chosen to suit your quality, cost, and privacy needs.
- OpenAI
- Claude
- Gemini
- Llama
- Mistral
- Hugging Face
-
Python
-
TypeScript
-
Next.js
-
React
-
Node.js
-
Azure
-
AWS
-
Google Cloud
-
Docker
-
Security
Industries
Generative AI where content matters.
Sectors that produce large volumes of written material or customer communication see the quickest returns.
E-Commerce
Commerce technology connecting customers, inventory, payments, and retail operations.
EdTech
Learning and assessment platforms for institutions, enterprises, and growing communities.
Real estate management
Property platforms for portfolios, leasing, tenants, sales, and day-to-day operations.
FinTech
Payment, lending, and transaction platforms for always-on financial operations.
Pharma
Compliant platforms for pharma operations, distribution, and regulated workflows.
Technology and SaaS
SaaS platforms and AI products built to scale with users, features, and integrations.
FAQs
Generative AI questions.
Straight answers about generative ai with Agleam Labs.
Talk to an expert
Tell us what you are working on. A senior engineer will reply with honest advice and a suggested next step.
Book a call →What is the difference between generative AI and RAG? + −
Generative AI produces content from a model's general knowledge and your instructions. RAG adds a retrieval step so the model answers from your own documents. Many applications use both, and we advise which your use case needs.
How do you stop the model making things up? + −
We constrain the task, provide source material where facts matter, validate output structure, and run automated checks. For high-stakes content, a person reviews before anything is published.
Will our data be used to train public models? + −
We use enterprise API terms and configurations that exclude your data from provider training, and can deploy private or open-weight models when data must stay in your environment.
Can you add generative AI to our existing application? + −
Yes. Most of our generative work adds features to existing web, mobile, or internal applications through an API layer, without rebuilding the product.
Do we need fine-tuning? + −
Usually not at first. Good prompts, examples, and retrieval solve most needs. We recommend fine-tuning only when testing shows it gives a clear gain in quality, cost, or speed.
How long does a generative AI project take? + −
A focused feature or copilot typically takes four to ten weeks from prototype to production. Larger content platforms are delivered in phases.
Start a project
Let's shape your generative AI idea.
- A free consultation with a senior engineer
- Clear scope, timeline, and budget
- Confidential, with an NDA on request
- Support after delivery
Prefer email?
info@agleamlabs.comPut generative AI to work, safely.
Tell us what your team writes, reviews, or summarises most. We will suggest a practical first build and an estimate.