Keeping AI models
honest in production.
AI operations infrastructure (model monitoring, drift detection, retraining pipelines, and LLMOps) so the AI you deployed last quarter is still performing correctly today.
Ask AI about this service
Most AI projects focus on getting to a working model. Very few invest in the operational infrastructure that keeps the model working. Models drift. Input distributions change. New edge cases emerge. LLMs hallucinate in new ways as prompts evolve. Without monitoring and operational governance, AI systems that worked at deployment quietly degrade over months until they cause an incident. We build the AIOps layer that catches these problems before they become operational failures.
What we build.
Model Monitoring
Observability across infrastructure, data, and output metrics, with dashboards and alerts that surface degradation before it becomes a user-facing problem.
Drift Detection & Retraining
Pipelines that continuously compare production inputs against the training distribution and trigger a governed retraining workflow when drift crosses a threshold.
LLMOps
Operational infrastructure for production LLMs: prompt versioning, output quality evaluation, hallucination and safety monitoring, trace logging, and token cost tracking.
CI/CD for AI
ML deployment pipelines that treat model, architecture, and prompt changes as first-class software, gated by automated evaluation, canary deployment, and rollback.
Model health visible within a week.
From the day you engage us, not from a kickoff weeks later. Every milestone is a result you can see.
- Day 2
Models and metrics inventoried
- Day 7
Monitoring and drift alerts live on your key models
- Day 30
Governed retraining and release pipeline
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Fixed scope
Scope, price, and milestones are agreed before work starts.
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No surprises
Weekly demos and plain-language status. Changes are priced before they are built.
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Compliance from day one
Model lineage, approvals, and evaluation evidence are recorded automatically for audit.
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You own the IP
Code, models, and documentation transfer to you on successful handover.
Know the price, the date,
and the exit before we start.
SLA-governed retainer. Ongoing coverage at a fixed monthly fee. Each month closes with an SLA, incident, and improvement report. The same terms are written into every statement of work for aiops, so there is nothing to discover later.
- Quote Within 5 working days Price per milestone, dates, and named architect
- Priced One fixed fee per month Third-party costs listed separately, at cost. Invoiced in USD for international clients
- Paid On accepted working software Not on calendar dates or hours logged
- Changes Written and priced first Cost and date impact before work starts
You fund accepted work, never a promise.
Drag to move through the engagement. Money is released only as each gate is accepted, so what you have paid never runs ahead of what you can use.
- Month 1End of month 1
Coverage live, baseline report delivered, first improvement cycle shipped.
Accepted whenReport reviewed and accepted by your named owner.
Releases 25% - Month 2End of month 2
Monthly report: SLA met, incidents closed, improvements shipped, budget position.
Accepted whenReport reviewed and accepted by your named owner.
Releases 25% - Month 3End of month 3
Monthly report: SLA met, incidents closed, improvements shipped, budget position.
Accepted whenReport reviewed and accepted by your named owner.
Releases 25% - Month 4End of month 4
Monthly report: SLA met, incidents closed, improvements shipped, budget position.
Accepted whenReport reviewed and accepted by your named owner.
Releases 25%
Illustrative split. The exact percentages and dates are fixed in your statement of work.
When something goes wrong, who pays?
Every project meets surprises. What matters is who carries them. Pick one and compare how three commercial models respond.
The hours keep running while the team investigates. You pay for the surprise.
A change request is raised, usually priced at a premium and argued over.
We verified it before quoting, and the risk register held an allowance. Any overrun beyond it is ours.
It is absorbed into the hours. The date slips quietly and you learn at the invoice.
A change fee arrives, often as a lump sum with little explanation.
You get a written amendment with cost and date impact. Nothing starts until you approve it.
You pay for the difference. The estimate was only ever a guess.
The vendor reopens scope to recover margin, or cuts corners to hold it.
The monthly fee stays fixed. Extra effort to meet the agreed outcome is ours to carry.
It is billed on the calendar anyway. The status report says "on track".
The invoice follows the date in the contract, whether or not the work is done.
The monthly invoice is held until the report shows the agreed SLA and deliverables were met.
One price, built in the open.
The proposal itemises every feature or milestone, names its assumptions, and keeps a risk register with allowances. Allowances you never use are not billed.
- Number of systems or models covered
- Severity-based response times
- Hours of coverage
- Reporting and compliance cadence
Access and a system walkthrough are provided within 2 working days.
If falseCoverage starts when access does, and we say so on the first report.
The systems under coverage are those in the SOW inventory.
If falseAdding systems is priced and approved before they are covered.
Severity definitions and response times are agreed in the SLA.
If falseChanges to the SLA are a written amendment with the cost impact.
- Cloud and monitoring-tool usage
- Third-party licences
Invoiced in USD for international clients, with the currency fixed in the SOW. You own it. Code, models, documents, and credentials transfer on payment.
Built on a production-grade stack.
We pick tools proven in production and maintainable over long lifecycles - not whatever is trending. These are a few we reach for here; explore the full stack behind our engineering.
Explore our technology stack →
What buyers ask before they commit.
Straight answers on timelines, price, risk, and ownership, the same terms that appear in every proposal.
01What is AIOps?
AI operations and MLOps for keeping AI models honest in production. Model monitoring, drift detection, LLMOps, and production AI infrastructure management.
02How quickly does AIOps deliver a first result?
Model health visible within a week. Typical milestones are Day 2: models and metrics inventoried; Day 7: monitoring and drift alerts live on your key models; Day 30: governed retraining and release pipeline. Exact dates are written into the statement of work before you commit.
03How is AIOps priced?
SLA-governed retainer: one fixed fee per month, agreed up front. Ongoing coverage at a fixed monthly fee. Each month closes with an SLA, incident, and improvement report. Cloud hosting, licences, and usage costs are passed through at cost. International clients are invoiced in USD. The main price drivers are number of systems or models covered, severity-based response times, hours of coverage, reporting and compliance cadence.
04What happens if the scope changes or the work overruns?
The monthly fee stays fixed. Priorities can change between cycles, and any change that affects cost or the agreed service levels is quoted in writing before it is made. Extra effort needed to meet the agreed outcome is ours to carry.
05Who owns the code, models, and documentation?
You do. Code, models, and documentation transfer to you on successful handover. Code, architecture documents, deployment scripts, and credentials transfer on payment, with no licence back.
06How are security and compliance handled?
Model lineage, approvals, and evaluation evidence are recorded automatically for audit.
07Do you work with clients outside India?
Yes. We are based in Noida and work with clients around the world, delivering remotely. Calls and demos are scheduled in your time zone, proposals and contracts are in English, international clients are invoiced in USD, and every engagement follows the same fixed-scope terms regardless of where you are.
08When would you decline to fix a price?
If a system has no access, logs, or documentation, we say so before committing to an SLA, and we cover the first weeks as a fixed-fee stabilisation.
AI that keeps working after deployment.
If you have AI systems in production without monitoring, or are deploying AI and want to build the operational infrastructure alongside the model, we can design and implement the AIOps layer your system needs.