Loading…
Loading…
Computer vision, LLM and RAG systems, agentic AI, and applied ML, engineered for production and priced against deployment. Delivered from India at 50 to 70 percent below Western consulting rates, with ISO 27001 baseline and full IP transfer.
Six disciplines that cover the large majority of enterprise AI work. Every one of them ships to production or it does not ship.
Detection, classification, tracking, OCR, and video analytics trained on your imagery. This is the discipline behind our own product, so the pipeline from annotation to GPU inference to production alerting is one we operate daily.
Retrieval-augmented generation over your documents, domain-tuned language models, and copilots grounded in your live systems. Deployable against open-weight models on your own hardware when data cannot leave the building.
Agents that reason over a task, call real tools, and take action in your systems rather than only answering questions. Built with per-tool authorization, guardrails, and escalation paths for low-confidence decisions.
Forecasting, churn, anomaly detection, risk scoring, and optimization on your historical data. Scoped against a measurable business outcome rather than a model metric.
The part most programs underestimate. Ingestion, labelling strategy, feature pipelines, and quality gates. It routinely consumes 30 to 50 percent of the engineering budget, so we scope it explicitly rather than discovering it later.
Model monitoring, retraining pipelines, drift detection, A/B testing, and rollback. Models degrade in production. Without this layer, accuracy quietly decays for months before anyone notices.
Most enterprise AI programs die between the proof of concept and production. Our sequence is built to survive that gap rather than pretend it does not exist.
We define the outcome in measurable terms before any model work starts. Not "use AI in support" but "cut first-response time on billing tickets by 40 percent, measured monthly." Vague targets are the strongest predictor of a failed program.
We audit what data exists, what it is worth, and whether the problem is learnable at all. Sometimes the honest answer is that rules beat ML here, and we will say so before you spend the budget.
A time-boxed 4 to 8 week build that validates one hypothesis on real data. The deliverable is evidence, not a demo. It either clears the bar we agreed on or it does not.
Data pipelines, model training, MLOps, integration into the systems your team already uses, and the change-management work that decides whether anyone actually uses it. Priced from day one, not as unscoped follow-on.
Monitoring, retraining, and iteration against the original business metric. We hand over a system your team can run, with the documentation and runbooks to do it without us.
Custom AI is the right answer less often than vendors admit. Here is the split we use before taking an engagement.
The pattern that works for most enterprises is to buy the commodity layer and build only where the business differentiation actually lives. Our custom AI vs off-the-shelf guide covers the decision in depth.
Published ranges, not a discovery call to find out. Indian engineering economics put these 50 to 70 percent below equivalent US and Western European partners at the same rigor.
$25k to $80k
4 to 8 weeks
Validate one hypothesis on one dataset. Deliverable is a containerized model plus an honest read on whether the approach is feasible.
$80k to $300k
4 to 9 months
A production-grade system on a single business problem, with data pipelines, MLOps, integration, and adoption work included in scope.
$400k+
9 months+
Multi-model platform with real-time inference, edge or air-gap deployment, and federated governance across business domains.
Budget ongoing operations at 15 to 30 percent of the initial build cost annually. The full cost framework is in our real cost of AI development guide.
Visylix, our enterprise AI video management platform, runs 22 AI analytics and a fully on-premise copilot on customer infrastructure. The team that built it delivers your engagement.
Our AI work is validated by a platform we operate ourselves, not only by reference calls.
Models, code, pipelines, and weights are yours, deployable on infrastructure you control. No platform lock-in.
You meet the architects and engineers who will do the work during procurement, not just the sales team.
ISO 27001 baseline, ISO 42001 readiness, GDPR engineering, India DPDP Act, and NDAA 889 alignment.
Book a free AI audit and we will tell you honestly whether custom AI is the right call for your problem, what it would cost, and what could go wrong. If the answer is that you should buy instead of build, we will say that too.
Custom AI development is the process of building an AI system around a specific business problem rather than buying a general-purpose product. It covers problem specification, data engineering, model selection or training, evaluation, MLOps infrastructure, and integration into the systems your team already uses. The distinguishing feature of custom AI is that the model, the data pipeline, and the operational workflow are designed for one organization rather than for a market average.
A focused proof-of-concept that validates one hypothesis on one dataset typically costs $25,000 to $80,000 over 4 to 8 weeks. A production-grade custom AI system with data pipelines, MLOps tooling, and integration typically costs $80,000 to $300,000. A multi-model enterprise platform with real-time inference and edge deployment often exceeds $500,000. Ongoing operational costs including retraining, monitoring, and infrastructure should be budgeted at 15 to 30 percent of the initial build cost annually. Indian partners deliver equivalent engineering rigor at 50 to 70 percent below US and Western European rates.
Build custom when the problem is specific to your operation, when your data is a genuine advantage, when the workflow integration is the hard part, or when data sovereignty rules out a cloud product. Buy off-the-shelf when the problem is generic (transcription, translation, common document extraction), when a mature product already solves it, or when speed matters more than fit. Most enterprises should buy the commodity layer and build only where the business differentiation actually lives.
A focused proof-of-concept typically runs 4 to 8 weeks. A production deployment typically runs 4 to 9 months depending on data readiness, integration surface, and compliance requirements. The single biggest schedule variable is data engineering, which routinely consumes 30 to 50 percent of the total engineering budget and is the most commonly underestimated part of the plan.
You do. Our engagements are structured so that the models, code, pipelines, and trained weights are the client's property, deployable on infrastructure you control. We do not require our platform, our cloud, or our tooling as a condition of the work, which means there is no lock-in cost after the engagement ends.
Yes. We deploy on your own infrastructure using open-weight models, local vector databases, and self-hosted inference. All data, model weights, and inference stay inside your network. This is the standard pattern for defence, government, banking, and healthcare buyers, and it is the same architecture we ship in our own product, Visylix, which runs 100 percent on-premise and is air-gap compatible.
We ship a product. Visylix, our enterprise AI video management platform, runs 22 AI analytics and a fully on-premise copilot on customer infrastructure. The team that built it delivers client engagements, so the engineering is validated by something we operate ourselves rather than only by slideware. We price against production deployment rather than consulting deliverables, and we name the engineers who will do the work during procurement.
ISO 27001 baseline security posture, ISO 42001 readiness for AI management systems, GDPR engineering for European buyers, India DPDP Act compliance, and NDAA 889 alignment. For regulated buyers we work to the sector framework that applies, including HIPAA for healthcare and CJIS for public safety.