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Most enterprise AI dies between the proof of concept and production. We do the unglamorous work that closes that gap: data engineering, MLOps, integration, and the adoption effort that decides whether anyone actually uses it.
None of these is the model. All of them are why programs succeed or stall.
Ingestion, labelling strategy, feature pipelines, and quality gates. This routinely consumes 30 to 50 percent of the engineering budget and is the most common reason a timeline slips. We scope it explicitly instead of discovering it in month three.
Model registry, retraining pipelines, drift detection, A/B testing, and rollback. Without this, accuracy decays quietly for months before anyone notices, and by then trust in the system is gone.
Wiring the model into the ERP, CRM, VMS, or line-of-business system where the decision actually gets made. A model behind a separate dashboard nobody opens produces no value.
The consistently under-budgeted line item. Training, workflow redesign, and escalation paths. A system that ships but is not used returns nothing, regardless of its accuracy.
The measurement that tells you whether it is working against the original business metric, not a model metric. Set up before launch, not retrofitted after someone asks for ROI.
Documentation, runbooks, and the training your team needs to operate the system without us. If you still need us to keep it alive in year two, we did the job badly.
If your AI program is stuck, it is almost certainly one of these. Naming it is the first step to fixing it.
It worked on a clean extract. Then data engineering, integration, monitoring, and adoption costs surfaced and the program stalled. Production typically costs 3 to 10 times the POC.
Nobody on the client side owns the model after launch. Drift goes unnoticed, alerts get ignored, and the system decays into shelfware within two quarters.
The cheapest line item to cut and the most expensive to omit. Without monitoring and retraining, accuracy degrades and the failure is invisible until it is a business incident.
Built on the vendor platform, operable only by the vendor. The AI works. The dependency compounds. Migration out costs more than the original build.
The full breakdown of why POCs fail and what production actually costs is in our real cost of AI development guide.
Indian engineering economics put these 50 to 70 percent below equivalent US and Western European partners at the same rigor.
$80k to $300k
Take a validated proof of concept into a production system with data pipelines, MLOps, integration, and adoption work in scope from day one.
From $40k
Assessment plus remediation of a stalled build. We start by telling you honestly whether to finish it or rebuild it.
15 to 30% of build, annually
Monitoring, retraining, and iteration against the original business metric. Budget it from day one, not after the first incident.
Visylix, our enterprise AI video platform, runs 22 AI analytics and a fully on-premise copilot on customer infrastructure. We have shipped the implementation problem ourselves, not only advised on it.
We are paid for a working system, not for reports and readouts.
We deploy into your cloud, hardware, or air-gapped environment. No platform dependency.
Models, pipelines, and code are yours, operable by your team after handover.
ISO 27001 baseline, ISO 42001 readiness, GDPR, India DPDP Act, NDAA 889 alignment.
Book a free AI audit. We will tell you what is actually blocking the program, what it would cost to finish, and whether finishing is the right call at all.
AI implementation services take a validated AI idea and turn it into a system that runs in production and is actually used. The work covers data engineering, model deployment, MLOps infrastructure, integration into the systems your team already uses, and the change management that determines adoption. Implementation is distinct from AI strategy, which decides what to build, and from a proof of concept, which only tests whether it is feasible.
The most common failure is a proof of concept that was never scoped to become a product. The pilot works on a clean dataset, then the cost of data engineering, integration, monitoring, and user adoption surfaces and the program stalls. Other recurring causes are no operational owner, no MLOps so accuracy quietly decays, and building a system the client team cannot run without the vendor. The engagement structure predicts the outcome more reliably than the model choice.
Taking a validated POC into production typically costs $80,000 to $300,000 depending on data readiness, integration surface, and compliance requirements. Budget ongoing operations at 15 to 30 percent of the build cost annually for monitoring, retraining, and infrastructure. Production deployment usually costs 3 to 10 times the POC, which is the single most common budgeting mistake we see.
A production deployment typically runs 4 to 9 months. Data engineering is the biggest schedule variable and routinely consumes 30 to 50 percent of the engineering budget. Compliance-heavy sectors add time for security review, DPIA, and audit evidence.
Yes, and we prefer it. We deploy into your cloud, your on-premise hardware, or an air-gapped environment using open-weight models and self-hosted inference where required. We do not require our platform or tooling as a condition of the work, so there is no lock-in cost after the engagement ends and your team can operate the system without us.
Yes. Rescue engagements are common. We start with an honest assessment of what exists: whether the model is salvageable, whether the data pipeline is sound, and what it would cost to finish versus rebuild. Sometimes the answer is that the previous work should be kept and completed, and sometimes it is that the foundation will not hold. We tell you which before you commit budget.