Staff spend hours searching for answers
Policies, product manuals, SOPs and past proposals are spread across shared drives and email. People ask colleagues instead of finding the document, and answers vary depending on who you ask.
SERVICES / AI & AUTOMATION
Custom AI and LLM Application Development for Real Business Tasks
RDS Innovations builds AI capabilities into software your team and customers already use. We start with one clearly defined task, such as answering questions from your policy documents or pulling fields out of invoices, and build it with testing, guardrails and cost tracking from day one.
- Use-case and feasibility assessment
- Knowledge preparation and retrieval (RAG)
- Document extraction and classification
- AI search and summarization features
IN PLAIN LANGUAGE
AI Development, explained simply.
Most useful business AI today is built on large language models (LLMs) that are accessed through an API from a model provider. The model itself is general purpose. The value comes from what is built around it: which documents it can read, what instructions it follows, how its output is checked, and where that output goes next in your software.
A common pattern is retrieval-augmented generation, usually shortened to RAG. Instead of training a model on your data, the system searches your approved documents first, picks the most relevant passages, and asks the model to answer using only those passages. This keeps answers tied to your own material, lets the system show which document an answer came from, and means updating the knowledge is as simple as updating the documents.
Other common capabilities include extracting structured fields from unstructured files (purchase orders, resumes, contracts, lab reports), classifying incoming items into categories, summarizing long records for a reviewer, and search that understands meaning rather than exact keywords. Each of these can sit inside a web application, an admin panel, a CRM or a mobile app.
AI output can be wrong, and model behavior can change when a provider updates a model. That is why we treat evaluation as part of the build: a set of real test questions or documents with expected results, checked before launch and again after changes. Model and API usage is billed by the provider based on volume, so we estimate and monitor it as part of the project.
PROBLEMS IT SOLVES
Why businesses invest in it.
Manual data entry from documents
Teams re-type details from PDFs, scanned forms and emails into a system. It is slow, error-prone and hard to scale when volumes rise during busy months.
Keyword search misses what users mean
Customers search your catalog or help center with their own words, and a basic keyword search returns nothing useful. Relevant results exist but are not found.
Long records nobody has time to read
Case histories, ticket threads and meeting notes grow long. Managers need a short, accurate summary before making a decision, but writing one takes time.
AI experiments that never reach production
A demo built in a chat tool looked promising, but there is no way to connect it to your data, control who sees what, measure accuracy or predict monthly cost.
IS IT RIGHT FOR YOU?
Who needs it, and when.
Who it is for
- Product companies adding an AI feature to an existing web or mobile app
- Organizations with large document libraries that staff need to search and reference
- Operations teams that process a steady flow of forms, invoices or applications
- Founders validating whether an AI-based product idea works with real data
When it is the right time
- You can name a specific, repeated task where AI would save time or improve consistency
- You have the documents or data the AI needs, and the right to use them
- A person can review AI output where mistakes would be costly
- Ready-made AI tools cannot reach your data or fit inside your existing software
WHAT'S INCLUDED
Features, deliverables and capabilities.
Scope is agreed in writing for every project. These are the building blocks we typically plan with you.
Use-case and feasibility assessment
A short written assessment of the task, the data available, expected accuracy, risks, a rough usage cost estimate and whether AI is the right tool at all.
Knowledge preparation and retrieval (RAG)
Cleaning and splitting documents, creating embeddings (numeric representations of meaning), setting up vector search and keeping the index in sync when documents change.
Document extraction and classification
Pipelines that read uploaded files, extract agreed fields into a structured format, assign categories, and flag low-confidence results for a person to check.
AI search and summarization features
Meaning-based search across your content, plus summaries of records or documents, delivered as features inside your app with source references where possible.
Evaluation test sets
A library of representative questions and documents with expected answers, used to measure quality before launch and to catch regressions after prompt or model changes.
Guardrails and access control
Rules on what the AI may answer, refusal behavior for out-of-scope questions, filtering of sensitive fields, and retrieval that respects each user's permissions.
Monitoring and cost tracking
Logging of requests, response times, failures and token usage, so you can see quality trends and monthly model costs instead of guessing.
USE CASES
How businesses use it.
Representative examples of the work, described generically. They are not client case studies.
Policy and SOP question answering
Employees ask a question in plain language and receive an answer drawn from the current HR policy or operating procedure, with a link to the source section.
Invoice and purchase order extraction
Uploaded supplier invoices are read automatically. Vendor, date, line items and tax amounts are extracted, validated against rules and queued for approval.
Resume screening support
Incoming resumes are parsed into skills, experience and location fields so recruiters can filter and compare candidates. Final shortlisting stays with the recruiter.
Product catalog search
Shoppers describe what they need, such as a gift for a runner under a budget, and the store returns relevant products even when the exact words do not match.
Case and ticket summaries
A long support or service history is condensed into a short summary of the issue, actions taken and open questions before a supervisor reviews it.
DELIVERY PROCESS
How we deliver it.
Decisions stay visible, important workflows are validated early, and delivery is shaped around the agreed scope.
- 01
Define the task and success criteria
Agree on exactly what the AI should do, what a good result looks like, what mistakes are acceptable, and who reviews output.
- 02
Collect sample data and build a test set
Gather real documents and questions, remove anything that should not be used, and write expected answers to measure against.
- 03
Build a working prototype
Connect a model API, retrieval and prompts, then measure results against the test set and compare approaches before committing.
- 04
Integrate into your software
Add the feature to your web app, admin panel or API with user permissions, error handling, logging and a way to report bad answers.
- 05
Launch, monitor and refine
Release to a limited group first, review logs and feedback, adjust prompts or retrieval, and track usage costs month to month.
DECISION GUIDE
Off-the-shelf AI tool, custom API-based feature, or fine-tuning?
There are three broad ways to use AI in a business. The right one depends on how specific the task is, where your data lives and how the result must fit into your existing work.
Off-the-shelf AI tool
Best for: General writing, brainstorming and individual productivity where no company system needs to be connected.
Subscription chat and writing assistants are quick to start and need no development. They cannot easily reach your databases, follow your permission rules or run inside your own product, and you have limited control over how data is handled.
Custom feature built on a model API
Best for: Specific, repeated tasks that use your own documents or data and must run inside your software.
This is where most business projects should start. A general model is combined with retrieval over your content, clear instructions and validation rules. It is faster to build than training a model, easy to update when documents change, and lets you switch model providers later.
Fine-tuning a model
Best for: Narrow tasks with a large set of high-quality examples where a consistent style or format is essential.
Fine-tuning adjusts a model using your example inputs and outputs. It is rarely the right first step: it needs substantial clean training data, adds cost, must be repeated when base models change, and does not by itself give the model up-to-date knowledge. We usually recommend trying retrieval and better instructions first.
Benefits, technologies and integrations
BENEFITS
- Answers tied to your own approved documents rather than general internet knowledge
- Less manual re-typing of data from forms and files
- Search that understands what users mean, not only the exact words they type
- Measured quality through a test set, instead of judging AI on a few demos
- Visibility into monthly model usage and cost
- AI that works inside your existing software rather than in a separate tool
TECHNOLOGIES WE USE
PythonDjangoLLM APIsEmbeddingsVector searchPostgreSQLMongoDBREST APIsNext.jsReactDocker
INTEGRATIONS & CUSTOMIZATION
- Document sources such as Google Workspace drives and shared folders
- Existing databases and internal admin panels
- CRM and helpdesk records through their APIs
- File upload flows in web and mobile apps
- Model providers' APIs, chosen per task and budget
- Approval queues where people review AI output
- Usage dashboards for request volume and token costs
PLANNING
Considerations, cost and timeline.
Limitations & implementation considerations
- AI output can be incorrect or incomplete. Tasks with legal, financial or medical impact need human review before anything is acted on.
- Quality depends heavily on the documents and data available. Outdated, duplicated or poorly scanned files produce weaker results.
- Model and API usage is billed by the provider based on volume and is a recurring cost that grows with usage.
- Sending business or personal data to a model provider must be assessed with you and your legal advisers, including obligations under India's Digital Personal Data Protection Act, 2023.
- Prompts, retrieval and test sets need ongoing maintenance as documents, users and models change.
What affects cost
- Number and complexity of AI tasks in scope
- Volume, format and condition of the documents or data
- Accuracy requirements and the size of the evaluation test set
- Integration with existing software, permissions and user roles
- Expected request volume, which drives model usage costs billed separately
- Monitoring, review queues and ongoing tuning after launch
Typical timeline
A focused prototype for one task typically takes a few weeks, depending on data readiness. Moving it into production with integration, permissions and monitoring usually adds several more weeks, and larger AI programs are delivered in phases.
INDUSTRY APPLICATIONS
Where it is commonly used.
FAQ
Questions about ai development.
Do we need to train our own AI model?
Usually not. Most business tasks work well with an existing model accessed through an API, combined with search over your own documents. Training or fine-tuning is considered only when there is a clear reason and enough quality data.
Can the AI answer only from our documents?
It can be instructed and designed to answer from retrieved passages of your documents and to say when it cannot find an answer. This reduces off-topic or invented answers, but it does not remove the possibility of mistakes, so testing and review remain important.
How do you measure whether the AI is accurate enough?
We build a test set of real questions or documents with expected answers and measure results against it before launch. The same test set is rerun after any change to prompts, retrieval or model, so quality changes are visible.
What will the AI cost us each month after launch?
Model and API usage is billed by the provider based on how many requests and how much text is processed. We estimate this during the feasibility stage and set up tracking so you can see actual usage. These charges are separate from development fees.
Is our data safe when it is sent to an AI provider?
It depends on the provider, the plan and the data involved. We review provider data-handling terms with you, limit what is sent to what the task needs, and recommend that you involve legal advisers for personal or regulated data.
LEARN MORE
Guides and articles.
NEXT STEP
Let's discuss your ai development project.
Tell us what you are trying to build, fix or improve. We will reply with questions, a suggested approach and a realistic estimate.