AI systems degrade silently: models drift, providers change APIs, costs creep, and yesterday's accurate answers quietly go stale. Our AI maintenance and support services keep deployed AI accurate, current, and economical.
Whether we built it or you inherited it, your chatbots, models, and automations get monitoring, evaluation, retraining, and a team that answers when behavior surprises you.
Output quality tracked continuously - regressions caught before users complain.
RAG sources, prompts, and content kept current as your business changes.
Model deprecations and API changes absorbed without breakage.
Usage audited monthly; caching and tiering tuned as patterns shift.
Models refreshed on schedule or triggered by measured drift.
A real AI team on call when outputs surprise or incidents hit.
We identify where AI genuinely saves money or creates revenue in your business - and where it does not.
Your data, systems, and constraints are assessed to pick the right approach and models.
A working proof-of-concept measured against real business metrics before full investment.
Production-grade AI integrated into your existing tools, workflows, and applications.
Models and automations are monitored, retrained, and refined as your data evolves.
We start from ROI, not hype. If a simple script beats a model, we will tell you.
GPT-class LLMs, open-source models, and classic ML - chosen per problem, not per fashion.
AI that plugs into the tools you already use - websites, CRMs, ERPs, and messaging apps.
Architectures that respect your data ownership, compliance needs, and customer privacy.
Start with a focused pilot from Kathmandu at a fraction of Western agency rates.
AI is not fire-and-forget. We monitor, retrain, and tune so quality never silently degrades.
AI Maintenance & Support services are available across Nepal - with on-the-ground support in these cities - and remotely for clients worldwide. Explore more AI Services services or talk to our team about your project.
Because its environment moves constantly: your business facts change (stale RAG answers), user behavior shifts (drift), providers deprecate models and alter behavior mid-flight, and costs mutate with usage patterns. Normal software fails loudly; AI fails plausibly - monitoring is the only way to know.
Yes - AI system takeover parallels our software rescue work: we audit the pipeline (prompts, retrieval, models, integration), establish evaluation baselines, document the undocumented, and assume operations. Abandoned chatbots and orphaned automations are increasingly common arrivals.
Layered signals: technical health (latency, errors, token spend), output quality (evaluation sets scored on schedule, drift statistics), and business outcomes (resolution rates, handoff frequency, user feedback). Dashboards you can see, alerts when thresholds break, and monthly reviews in plain language.
Under maintenance, a routine event: we test candidates against your evaluation set, tune prompts for the successor, migrate behind the abstraction layer, and verify before cutover. Without maintenance, deprecations are how AI features silently break - this scenario alone justifies the plan for most clients.
Scaled to system criticality - monitoring-and-response plans for a chatbot start modestly; multi-system portfolios with retraining cycles cost more. Benchmark it against the failure mode: wrong answers to customers or a dead automation rediscovered weeks later. Prevention is embarrassingly cheaper.
Get a free consultation and a no-obligation quote from our team in Kathmandu.
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