AI inside your existing products
Add AI features to .NET, Angular, React and mobile apps without a rewrite. We build a small AI service layer your app calls, so models can change later without touching your code.
Generative and agentic AI, built for production
We design, build and integrate AI that works inside your real systems. Secure, measurable and easy to maintain, with your people in control of every decision that matters.
Generative AI applications
Assistants, content generation, summarisation and document Q&A, grounded in your own data with retrieval-augmented generation (RAG), so answers come with sources.
- Chat assistants for staff or customers
- Search and Q&A over documents, wikis and tickets
- Summaries, drafts and reports in your format
- Model choice across Claude, Azure OpenAI and Amazon Bedrock
Your documents and dataPolicies, manuals, SQL Server, SharePoint
Search indexAzure AI Search or a vector store
Language modelRetrieves context, then writes the answer
Answer with sourcesEvery claim links back to a document
Goal"Reconcile this month's vendor invoices"
Agent plans the stepsBreaks the goal into tasks it can check
Calls your toolsERP API, database, email, MCP servers
A person approvesAnything that spends money or changes records
Done, with a full logEvery step is recorded for audit
Agentic AI and workflow automation
Agents that complete multi-step work across your systems: reading, deciding, calling APIs and asking a person before any action you mark as sensitive.
- Tool use through your APIs and Model Context Protocol (MCP) servers
- Human-in-the-loop approvals and clear audit logs
- Built on Microsoft Agent Framework and Semantic Kernel for .NET, or other frameworks where they fit better
- Runs on Azure or AWS, inside your security boundary
More ways we put AI to work
AI copilots for teams
Internal knowledge assistants, support copilots and sales helpers that know your products, available in your web app, Teams or Slack.
Data and AI foundations
Clean, labelled data is what makes AI accurate. Our own Annotation Portal, data pipelines and analytics dashboards get your data ready for training and evaluation.
About the Annotation PortalResponsible AI and governance
Every AI feature ships with the checks that keep it safe and affordable.
- Evaluation sets that measure answer quality
- Guardrails and prompt-injection defences
- Personal data masking and access control
- Cost and usage monitoring per feature
Use cases by department
Good first projects are narrow, frequent and easy to measure. These are the ones we are asked about most.
Invoice and document extraction
Pull vendor, amounts, dates and line items from PDFs and scans into your ERP. Low-confidence fields go to a reviewer instead of being guessed.
FinanceOperations
HR policy assistant
Instant, sourced answers to employee questions.
HR
Resume screening helper
Shortlists against the job spec, for a recruiter to confirm.
HR
Exam question generator
Question banks by topic and difficulty for teachers to review.
Education
Customer support agent
Answers common requests and escalates with context.
Support
Report-writing agent
Turns database queries into a weekly management report in your template, ready for a manager to check and send.
ManagementAnalytics
Code modernisation agent
Speeds up .NET Framework and AngularJS migrations.
IT
Sales assistant
Drafts proposals from your product catalogue and past wins.
Sales
From idea to production in three steps
Each step ends with a decision you make, so you only invest further when the results are proven.
AI discovery workshop
We map your processes, pick the highest-value use case and agree how success will be measured.
Proof of concept
A working prototype on your real data, with an evaluation report on quality, cost and risk.
Production rollout
Hardened, monitored and integrated into your systems, with training for your team.
Questions about AI projects
Is our data used to train AI models?
No. We use enterprise model services and settings where your prompts and data are not used for training, and we can keep everything inside your own Azure or AWS account.
Which AI models do you use?
We choose per use case, usually from Claude, Azure OpenAI and Amazon Bedrock. The AI layer we build lets you switch models later without rewriting your application.
How do you stop the AI from making things up?
We ground answers in your own documents, show the sources, test against an evaluation set before launch, and route low-confidence cases to a person.
What does an AI feature cost to run?
It depends on volume and model choice. The proof of concept includes a running-cost estimate, and production features come with usage monitoring and limits.
Who owns the code and the prompts?
You do. All code, prompts, evaluation sets and documentation are handed over under our standard IP assignment and NDA.
Let's find the right first AI project for you
Start a project
Book a free 30-minute call with a solution architect. We'll suggest an approach, a team and a transparent estimate.
Book a free consultationHire engineers
Add forward deployed engineers or a dedicated team that works in your repo, your stand-ups and your time zone.
See engagement modelsPrefer to talk now? Call +91 99098 71091 or message us on WhatsApp.