The questions buyers actually ask, answered the way we answer them on the first call. No brochure language — if the honest answer is “it depends on scope”, the page says so and explains what it depends on.
Straight answers on AI, software and how we work
These are the questions buyers actually ask on the first call, answered the way we answer them in that call. If your question is not here, ask it directly — you will get a specific answer, not a brochure.
AI development
Working software, not a proof of concept. In practice: an AI agent or assistant connected to your real data sources, with permissioning, guardrails, an evaluation set and monitoring, deployed on your infrastructure and handed over with the source code. If a use case cannot survive those requirements, we say so before the project starts.
We are model-agnostic and choose per task: OpenAI, Anthropic and Google Gemini for hosted models, and open-source LLMs (including Llama-family models) when data must stay inside your own network. The architecture keeps the model behind an interface so a better or cheaper model can be swapped without rewriting the application.
RAG (retrieval-augmented generation) retrieves the relevant passages from your own documents or databases and gives them to the model as context before it answers. It matters because it makes answers traceable to a source, keeps private data out of model training, and lets you update knowledge by updating documents rather than retraining a model. See RAG application development.
An AI voice agent handles inbound or outbound calls using speech recognition, a language model and speech synthesis. Reliability comes from design: tight latency budgets, scripted escalation to a human, call recording and outcome tagging, and a fallback path when confidence is low. Ours hand off to a person with the full transcript rather than guessing.
By constraining it. Retrieval is limited to approved sources, outputs are validated against schemas, irreversible actions require confirmation, and every low-confidence or out-of-policy case is routed to a human. We also build an evaluation set from your real questions so accuracy is measured before and after each change, not assumed.
Healthcare & regulated software
We build HIPAA-ready systems: role-based access control, audit trails on every patient record access, encryption in transit and at rest, configurable data residency, and documented retention and de-identification procedures. Compliance belongs to the deployment, so we map controls to your specific clause list and state plainly where a control is not yet in place.
iCare is our rare disease management platform — patient registry, treatment and infusion tracking, drug inventory, clinical workflows and reporting. It is in production for a government rare disease programme covering haemophilia, thalassemia and sickle cell.
Yes, in phases. Most programmes start with controlled import and export, then add HL7-style messaging, and then FHIR resources where the receiving system supports them and where ABDM or national programme requirements call for it. We do not assume a full interoperability stack on day one.
An NDA is signed before discovery. AI integrations use enterprise APIs where your data is not used for training; where data cannot leave your environment we deploy self-hosted models in your cloud or on-premise. Repositories, databases and hosting are set up in your accounts from day one.
Cost & timeline
It depends on scope: how many systems and data sources must be integrated, how much automation and guardrail work is required, and how strict the compliance bar is. After a free 30-minute discovery call you get a fixed-price quote with written deliverables, and we flag the cost of any scope change before building it.
Most MVPs reach production in 4 to 8 weeks using two-week sprints with a live demo every week. A larger platform is usually delivered in phases of 6 to 12 weeks so you get working software early rather than a long silence.
A first modernized workflow typically ships in 8 to 16 weeks. Full retirement of an ERP-class system is normally a 9 to 18 month programme, released in phases you can stop between. See legacy software modernization.
Yes. Most clients start with a paid discovery or a single-workflow pilot, then scale once the first phase is in production. Each phase ships working software on its own, so stopping between phases is a legitimate outcome.
Process, code ownership & support
You do. The GitHub repository, database and hosting are created in your accounts. We work in your repo from the first commit, and you keep 100% root ownership with no vendor lock-in.
Weekly live demonstrations on the working software, with documented milestones. You get direct access to the engineers building the system rather than layered project management, and a written status you can forward internally.
Support continues. We offer maintenance and support with defined SLAs: monitoring, security patches, dependency upgrades, feature work and emergency response. See software maintenance & support.
Both. Startups usually need an MVP and a product partner; enterprises usually need integration, modernization or automation inside existing systems and governance. The process is the same — phased delivery, weekly demos, fixed-price scope per phase.