AI Automation
Workflow automation with LLMs, logging, cost caps, and review UIs. Integrations with tools you already use.
View AI Automation service detailsML · Data · Ops
Machine Learning Development
Training pipelines, model evaluation, and production inference, beyond AI buzzwords.
- Training pipelines, evaluation, and production inference that ships.
- Clean data engineering before model tuning hype.
- Batch and API inference with drift monitoring.
- Classical ML for structured prediction; LLMs when language dominates.
- Maintenance plans because models decay.
40+ projects since 2022
IST · daily sync
NDA-ready
WHAT WE OFFER
What we deliver for machine learning development
Core deliverables
- Data prep & labeling plans
- Model training & eval
- Batch/API inference
- Drift monitoring hooks
- Documentation for ops
Why teams choose this engagement
- Workflow mapping and human-in-the-loop design
- Prompt, tool, and retrieval architecture
- Cost monitoring and per-tenant budgets
- Evaluation sets before production rollout
CHALLENGES
Problems we solve in machine learning development
-
Models that never leave Jupyter
Training notebooks without deploy paths waste labeled data. We plan batch or API inference before the first experiment ends.
-
No baseline to beat
Teams ship models without measuring against simple rules. We agree metrics and holdout sets in discovery.
-
Training-serving skew
Features computed differently in batch and online break predictions silently. We align pipelines with tests.
-
Drift discovered by customers
Production ML needs monitoring hooks and retrain triggers, not quarterly manual checks.
OUR APPROACH
How we build machine learning development
Founder-led engineers in Surat (IST) with morning and end-of-day updates so distributed product owners stay in the loop.
ML for us is prediction and classification that ships, training pipelines, model evaluation, and monitoring when drift appears. Not slide decks about AI strategy.
We pair ML with solid data engineering so your models have clean inputs.
Teams with labeled data and a measurable prediction problem.
ML OPS
From labeled data to inference
We focus on measurable prediction problems: data prep, training, evaluation, and production inference with docs ops can run without the original notebook author.
- Holdout metrics agreed before training spend
- Batch or API inference with staging benchmarks
- Labeling plans when data quality is the bottleneck
PRODUCTION
Models you can monitor
ML in production needs drift checks, rollback paths, and feature parity between train and serve. We wire hooks your on-call can act on, not dashboards nobody opens.
- Retrain triggers documented with ownership
- Feature stores or pipelines tested for skew
- Honest scoping when data is not ready for ML
INDUSTRIES
Where we apply machine learning development
Vertical experience from shipped products, not generic claims.
WHY US
Why teams choose us for machine learning development
Six reasons founders and product leads pick us over a generalist shop - scoped to how we deliver this engagement.
-
Labeled data discipline
We pause when labels are missing, not fake progress.
-
GPU cost aware
Spot/preemptible training where safe, sized jobs otherwise.
-
Production monitoring
Drift alerts and retrain triggers discussed upfront.
-
No AGI promises
Honest timelines for real prediction problems.
-
Eval before rollout
Golden sets and abstain rules before real users hit the feature.
-
Integrates with your stack
CRM, docs, and tickets - not a standalone chat box nobody adopts.
HONEST FIT
Is this for you?
Good fit
- You have labeled data or a clear labeling plan.
- You need batch or API inference in production.
- You understand models need maintenance.
- You have labeled data or a realistic labeling plan.
- You need batch or API inference with monitoring for drift.
- You understand models require maintenance after launch.
Probably not
- You want AGI in a quarter with no data.
- You confuse ML with a ChatGPT wrapper only.
- You want AGI-level results in one quarter with no data.
- You confuse ML with a ChatGPT wrapper only.
- You refuse access to production data for evaluation.
HOW WE WORK
Delivery process for machine learning development
How we take ML from labeled data to monitored production inference.
We document inputs, outputs, escalation paths, and data boundaries before any model keys go live. Cost caps and human review rules agreed in writing, not as a post-launch patch.
Model routing, retrieval strategy, golden test sets, and per-tenant spend limits defined upfront. Evaluation criteria signed off before pilot traffic hits staging.
Human-in-the-loop UI, logging, and token budgets on staging - real CRM, docs, and ticket integrations. Not notebook demos that break when production traffic arrives.
Abstain rules, fallback models, rate limits, and audit trails reviewed with your team. Failure modes and escalation paths tested before full rollout.
-
Data audit
We document inputs, outputs, escalation paths, and data boundaries before any model keys go live. Cost caps and human review rules agreed in writing, not as a post-launch patch.
-
Train and evaluate
Model routing, retrieval strategy, golden test sets, and per-tenant spend limits defined upfront. Evaluation criteria signed off before pilot traffic hits staging.
-
Serve safely
Human-in-the-loop UI, logging, and token budgets on staging - real CRM, docs, and ticket integrations. Not notebook demos that break when production traffic arrives.
-
Monitor
Abstain rules, fallback models, rate limits, and audit trails reviewed with your team. Failure modes and escalation paths tested before full rollout.
TECHNOLOGIES
Stack for machine learning development
Tools and runtimes we use on this type of engagement - chosen for production delivery, not slide-deck logos.
- Python
- scikit-learn
- pandas
- PostgreSQL
WORKFLOW
How we work on machine learning development
-
Review queues
Human escalation UI for high-stakes model outputs.
-
Cost dashboards
Token spend and error rates visible to your team.
-
Incident channel
Fast loop when models drift or integrations fail.
-
Eval sets
Golden questions updated as product scope evolves.
DEPLOYMENT
Production discipline for machine learning development
-
Feature flags
Model routes and prompt versions toggled without redeploying the whole app. Roll back a bad prompt in minutes, not hours.
-
Spend caps
Per-tenant and global token limits enforced before production traffic. Finance sees dashboards, not surprise invoices.
-
Audit logs
Prompt and tool-call history retained per your policy and NDA. Retention windows and redaction rules documented at launch.
-
Review gates
Human approval on outputs above your risk threshold. Escalation UI wired before autonomous paths go live.
OUTCOMES
Track record from machine learning development
Metrics from shipped products and active engagements - not slide-deck claims.
- 40+
- AI features in production
- Guardrails
- Human review on day one
- IST
- Morning & EOD sync
- Audit
- Logs and cost caps wired
CASE STUDIES
Proof from machine learning development
Real products we shipped for founders in the US, UK, and Europe.
Ops and product leaders want evidence we ship LLM features with guardrails - logging, cost caps, and human review - not notebook demos.
-
LLM demo failed in production
AstroSure shows LLM features with structured data, review paths, and cost controls.
-
Finance saw an API bill spike
We ship token budgets and logging before real users - patterns reused below.
-
No human review path
Case studies include escalation UI and audit trails, not fully autonomous agents.
Hire us
Engagement models for machine learning development
Machine learning projects with discovery, fixed training phases, or squad time for inference APIs.
-
Fixed-scope project
Discovery, written requirements, and milestone billing. Best for MVPs, redesigns, and integrations with a defined end state.
- Duration: Phased milestones
- Working: Sprint plan agreed upfront
- Billing: Per milestone or phase
- Timeline: Based on signed scope
-
Dedicated squad
A focused engineering squad on your product: weekly demos, shared backlog, and one accountable team when scope evolves.
- Duration: 8 hrs/day · 5 days/week
- Working: ~160 hrs/month capacity
- Billing: Monthly invoice
- Timeline: Sprint-based delivery
-
Part-time retainer
Smaller monthly hour buckets for fixes, dependency updates, and enhancements, with the same engineers when possible.
- Duration: 4 hrs/day · 5 days/week
- Working: ~80 hrs/month
- Billing: Monthly retainer
- Timeline: Ongoing support window
Mutual NDA before codebase access
Morning & EOD IST sync
Written scope before sprint one
FAQ
Questions about machine learning development
What prospects ask on a first call about this service: scope, timelines, fit, and how we work.
- Scope & pricing
- Delivery process
- Handover & IP
- NDA & quality gates
5 questions
How is ML scoping different from adding a chatbot?
We define labels, data sources, evaluation metrics, and retrain cadence before model work starts.
Do you build training pipelines or only inference APIs?
Both when needed. Many engagements start with batch inference and monitoring, then add retraining when data grows.
Where does model training run?
Your cloud or ours per contract, with cost estimates and data residency documented in discovery.
How do you monitor ML models in production?
Drift signals, latency, error rates, and human review queues when confidence drops below threshold.
Can you work with our data science team?
Yes. We productionize notebooks: APIs, schedules, tests, and deploy paths your DS team can extend.
GET STARTED
Need ML in production? Let's validate data.
Explain the decision you're automating, data available, and accuracy bar. We scope baselines, monitoring, and retraining - not notebook accuracy that fails live.
- Data quality and labeling called out early.
- Monitoring drift after launch.
