Technology
In short
Sumedicon builds practical AI into the software businesses already use: language model integrations that automate routine work, document data extraction, demand forecasting, anomaly and fraud detection, and similarity search. We start from a specific business problem, check the data is ready, keep people in the loop for important decisions, and measure whether the result is worth running.
Written by the Sumedicon team, Lucknow · Updated
AI integration means adding a model that predicts, classifies, extracts or generates something into a working business system, so that the output turns up where your staff already work. It is different from buying a chatbot subscription. The model might be a large language model (LLM) reading emails and drafting replies, a classifier flagging unusual transactions, or a forecasting model estimating next month's stock needs. On its own a model does nothing useful; it becomes useful when it is connected to your data, your screens, your approvals and your audit trail.
Most of the work in an AI project is ordinary software engineering: collecting and cleaning data, building APIs, designing review screens, handling errors and monitoring results. We treat it that way, using the same process and standards as any other project we deliver.
The requests that lead to good AI projects usually sound like this: our team spends hours every day typing details from invoices, forms or reports into the system; we only find suspicious transactions after the customer complains; we keep running out of some items and overstocking others; our researchers cannot search our own records by meaning, only by exact keywords; or our staff answer the same questions from customers all day.
What these share is a repetitive task with plenty of examples, where a good-enough answer most of the time saves real effort, and where a person can check the cases the model is unsure about. Requests that start with "we need to use AI somewhere" without a specific task tend to produce demos rather than lasting tools, so discovery always begins with the task and the cost of doing it today.
Depending on the problem, an AI integration project with us can include:
Discovery comes first and matters more here than in most projects. We look at the task, the data you hold, how the work is done today and what a mistake would cost. Often we run a short data review or a small trial on real samples, so both sides can judge whether the idea is feasible before committing to a full build. You then receive a written scope and a fixed quote to agree before the main work starts.
Design covers the data model, the API contracts and the clickable screens your team will use, including how uncertain cases are shown for review. You sign this off before production code. Development runs in two-week sprints, each ending in a working demo using your own data where possible. Every change is tested and reviewed, and for AI components we also keep a fixed evaluation set so we can see whether a change makes results better or worse.
At launch we set up monitoring of both the software and the model's output, then hand over the code, repository, prompts, evaluation sets and documentation, all of which you own. A support agreement covers retraining, prompt updates and model provider changes after launch.
Data readiness decides most AI projects before a model is chosen. A fraud model needs past transactions with confirmed fraud marked. A forecast needs consistent history long enough to show seasonal patterns. A document extraction tool needs sample documents in the layouts you actually receive. A knowledge base assistant needs current, accurate documents, because it will repeat whatever it is given.
Common gaps are records split across spreadsheets and systems, inconsistent product or customer codes, missing outcome labels, and history lost when an old system was replaced. None of these is unusual, and fixing them often brings value on its own through cleaner reporting. We tell you plainly what we find and whether the project should start with data work rather than a model.
Every model makes mistakes, so we design for them. Outputs come with a confidence level, low-confidence cases go to a person, and decisions that affect money, health, credit or someone's job stay with people. Staff can correct the model, and those corrections become examples for improving it. For fraud detection, for instance, the system flags and prioritises transactions, while your analysts decide what action to take.
Privacy is planned from the start. We send only the data a task needs, mask or remove personal details where possible, and choose between hosted model APIs and models running in your own cloud based on how sensitive the data is. We check provider terms on data retention and training, keep access logs, and design with India's Digital Personal Data Protection Act and your sector's rules in mind. Your legal advisers make the final call on compliance.
Before building, we agree on a baseline: how long the task takes today, how many errors happen, or how many cases are caught. After launch we report against it. Typical measures are hours of manual work saved, extraction accuracy on a held-out sample, the share of cases that still need review, false alarm and miss rates for detection models, forecast error compared with the current method, and the running cost per request or per month.
If the numbers show the model is not paying for itself, we say so and suggest whether to improve it, narrow its scope or switch it off. We do not quote expected accuracy or savings before seeing your data, because any such figure would be a guess.
Some problems are better solved without a model, and part of our job is to say so. Good reasons to skip AI include:
Our AI work suits organisations with a repetitive, data-heavy task: finance and financial services firms handling large transaction volumes, healthcare providers dealing with forms and records, manufacturers and logistics companies planning stock and capacity, research teams searching large databases, and SaaS founders adding AI features to a product. We have built vector similarity search over a chemical database for a research client and anomaly detection on card transactions for a financial services company.
We work mainly in Python for data and model work, with Node.js or Laravel where the AI feature sits inside an existing application. Data lives in PostgreSQL, MySQL or MongoDB, with Redis for queues and caching, and vector search through embeddings stored alongside your records. We integrate hosted LLM APIs or open models depending on cost and privacy needs, package everything with Docker and usually deploy on AWS.
We do not publish prices. Cost depends on how much data preparation is needed, whether an off-the-shelf model works or one must be trained on your data, the number of systems to connect, the volume of requests, the review screens your team needs, and privacy requirements such as hosting models in your own cloud. Ongoing model and hosting costs are estimated in the quote so there are no surprises. A fixed quote follows discovery.
A focused integration, such as extraction for one document type, can often reach a working pilot within a few sprints. Projects that need data clean-up, custom models or several integrations typically take several months, and we stage them so the first useful part goes live early.
Proof
A chemical research lab
Finds structurally similar compounds in a large chemical database using vector search.
See it in our portfolioA financial services company
Flags anomalous card transactions in real time while blocking fewer genuine customers.
See it in our portfolioFAQ
We take on practical projects tied to a specific task: LLM integrations that draft, summarise or classify; extraction of data from documents; forecasting from historical records; anomaly and fraud detection; and similarity search. We build them into your existing systems with review screens and monitoring. We avoid projects that have no clear task or no data to test against.
It depends on the task. LLM integrations for summarising or drafting can work with your documents and little else. Fraud detection and forecasting need a meaningful history with outcomes recorded. Document extraction needs realistic samples. During discovery we review what you have and tell you honestly whether it is enough, or whether data work should come first.
It can be, with the right design. We send only what a task needs, mask personal details where possible, check provider terms on retention and training, and log access. For sensitive data we can run models inside your own cloud account instead of using a hosted API. Your legal team confirms compliance with the DPDP Act and sector rules.
In our projects AI usually takes over the repetitive part of a task, such as data entry or first-pass screening, while people handle exceptions and decisions. Staff review uncertain results and correct the model, which also improves it. Decisions that affect money, health or individuals stay with people. Most clients use the saved time for work that needs judgement.
We cannot give an accuracy figure before seeing your data, and anyone who does is guessing. We build a test set from your real examples, measure the model against it, and report the results, including where it fails. Low-confidence outputs go to human review. After launch we keep measuring, because accuracy can drift as your data changes.
Yes, and that is most of what we do. We connect the AI component to your current application through an API, so results appear in the screens your team already uses. That might be a Laravel or Node.js system, an ERP, a CRM or a SaaS product. You own the code, prompts and evaluation sets at handover.
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Based in Lucknow, working with businesses across India and worldwide. Email info@sumedicon.in, Monday to Friday, 9 AM to 6 PM IST.
