Most AI models work in the demo and break the week they meet real data — drift sets in, accuracy slips, and no one owns the retraining. Building one that stays accurate in production takes data engineering, evaluation, and MLOps most teams don't have in-house. Banao builds custom AI/ML models — predictive, recommendation, anomaly detection, and fine-tuned LLMs — on the same stack we've run internally since 2017, and we own them through deployment, monitoring, and retraining.
General-purpose models are trained on the internet, not on your transactions, your customers, or your fraud patterns — so they generalize where you need precision. A custom model trained on your data closes that gap, but only if someone owns the unglamorous parts: data pipelines, evaluation harnesses, drift detection, and retraining. Banao has built these systems for Elisa's telecom support automation and Manentia AI's data workflows, and we run the same AI stack across our own 300-person operation — every pattern is stress-tested internally before it reaches a client.
Every engagement runs the full path — discovery, data engineering, training, evaluation, deployment, and the MLOps that keeps a model accurate after launch.
Talk to a Banao AI engineer
Know which use case will actually pay back
We map AI/ML opportunities against your data readiness and define KPIs, risks, and success metrics up front — so you fund the model that moves a number, not the one that demos well.
Training data your model can trust
We build the pipelines that aggregate, clean, and label your data at scale — because in production, accuracy is a data problem long before it's an algorithm problem.
Forecasts your operations can plan against
We build demand, churn, and risk-forecasting models on your historical and live data — the same model class behind the supply-chain platform Banao shipped for a logistics enterprise.
Recommendations that lift revenue per session
We build ranking and personalization engines like the one Banao built for Fuzu's career platform — tuned on your behavioral data, not a generic off-the-shelf library.
Catch the outlier before it costs you
We deploy real-time anomaly and fraud-detection models with the monitoring to keep false positives low — tuned to your transaction patterns and risk tolerance.
A private LLM grounded in your domain
We fine-tune or build LLMs on your data with retrieval grounding and guardrails so answers stay in-domain — the same RAG patterns Banao runs internally to qualify and brief its own deals.
A model your systems can actually call
We ship models to cloud, on-prem, or edge with versioned APIs, SDKs, and monitoring — wired into the apps, dashboards, and workflows your team already uses.
Accuracy that doesn't decay after launch
We build the retraining, drift-detection, and monitoring pipelines that keep a model reliable months after deployment — the part most vendors skip and most in-house teams underestimate.
Where we've shipped AI/ML models
Retail & E-commerce
Recommendation engines, demand forecasting, and churn prediction that move revenue per session and cut stockouts — built on your catalog and behavioral data.
Education & EdTech
Adaptive learning paths, dropout-risk prediction, and tutoring assistants — the kind of model behind work like Studylab AI.
Healthcare & Wellness
Diagnostic-assist, risk prediction, and imaging anomaly detection built with the audit trails regulated care demands — patterns from our work with Hummcare.
Finance & Insurance
Fraud detection, credit-risk modeling, and portfolio optimization with the explainability and audit logging BFSI compliance requires.
Manufacturing & Supply Chain
Predictive maintenance, demand forecasting, and production anomaly detection — the model class behind the Supply Chain Intelligence Platform Banao shipped for a logistics enterprise.
Customer Support & Productivity
Intent prediction, AI copilots, and NLP support automation that cut resolution times — the same stack behind Elisa's callbot, and behind Banao's own support operation.
Custom AI/ML models we've shipped
Conversational AI | NLP Solutions | Support Automation
Elisa AI Callbot
Elisa, a national telecom provider, faced a surge in customer requests during a crisis its manual support team couldn't absorb. Banao built an AI callbot and automation layer with intent detection and fallback routing, so routine requests resolved without an agent while complex ones escalated cleanly. The result was uninterrupted service through the spike and far less load on human agents.
Fuzu, a leading East African career platform, needed to match millions of candidates to relevant jobs without burying them in noise. Banao built an AI recommendation engine that ranks opportunities on each user's profile and behavior, not generic keyword overlap. Users surfaced more relevant roles faster, lifting engagement across the platform.
Supply chain
A logistics and manufacturing enterprise was running its supply chain on lagging reports and manual forecasts, leaving it exposed to stockouts and overstock. Banao built an end-to-end Supply Chain Intelligence Platform with demand forecasting and anomaly detection over live operational data. Planners moved from reacting to disruptions to anticipating them.
Data Tagging System
A data-driven enterprise was categorizing millions of records by hand, creating a bottleneck and inconsistent metadata. Banao built an AI and NLP tagging system that classifies content automatically, with a human-in-the-loop check on low-confidence cases. Document processing sped up and search relevance improved across the corpus.
Flow Legal AI
Legal teams at Flow Legal were losing hours to manual document review, contract drafting, and summarization. Banao built an AI system that automates review, drafting, and summarization with retrieval grounding, so outputs cite the source document rather than hallucinate. Lawyers reclaimed time for the judgment work software can't do.
Identify high-impact AI/ML opportunities. Define business goals, success metrics, and model requirements aligned to your industry. Why this matters: most failed AI projects were never tied to a business metric, so we won't train a model until success is defined in numbers you'd report to your board.
Collect, clean, and preprocess structured or unstructured data. Ensure quality, privacy, and readiness for model training. Why this matters: models fail in production far more often from messy data than from the wrong algorithm, so we harden the pipeline before anyone trains anything.
Choose the right algorithm or architecture (ML models, deep learning, or LLMs). Train or fine-tune for your specific use cases. Why this matters: we pick the smallest architecture that hits your accuracy target, instead of defaulting to the biggest model and handing you a bill and a latency problem you don't need.
Validate models with rigorous testing—accuracy, precision, recall, fairness, and bias checks to ensure reliability. Why this matters: a model that scores well on average can still fail the cases that matter most, so we test for fairness, bias, and edge-case behavior before it reaches a user.
Deploy models to cloud, on-prem, or edge environments. Integrate seamlessly with apps, dashboards, and business workflows. Why this matters: a model that isn't wired into your workflows is a science project, so we ship it where your team already works, with the APIs and monitoring to back it.
Enable real-time monitoring, drift detection, and automated retraining with MLOps pipelines for long-term performance. Why this matters: accuracy decays as the world drifts from the training data, so we detect drift and retrain automatically instead of waiting for users to notice.
Client Success Stories
Ananya Bhardwaj
VP, Strategy & Innovation, NovaChain
Harshil Jain
Head of Digital, MedNova Health
An AI roadmap we could actually execute
Banao scoped our top three AI use cases against our data readiness and gave us a sequenced roadmap with ROI projections, not a slide deck. We started building the highest-impact model within weeks instead of debating priorities for another quarter.
Here is what you will get for submitting your contact details.
45 minutes of free consultationA strict non-disclosure agreementFree market & competitive analysisSuggestions on revenue models & planningDetailed feature list documentNo obligation proposalAction plan to kick start your projectWe tried AI before and it didn't make it to production. Why would this be different?
Usually the model wasn't the problem — the data pipeline, evaluation, or integration was. We diagnose exactly where the last attempt broke, then design for that failure mode specifically. We've broken and fixed our own AI systems running them internally since 2017, and that scar tissue is part of what you're hiring.
How do you keep models from hallucinating or producing wrong outputs?
For LLMs we ground answers in your data with retrieval, add guardrails and moderation layers, and red-team before launch. For predictive and classification models we validate on accuracy, precision, recall, and bias, and monitor for drift in production so quality doesn't silently decay.
Who owns the model, the code, and the training data?
You do — 100%. Custom code, model weights, and training data are yours. We don't retain IP, sub-license it, or reuse your data to train anything for another client. We sign a mutual NDA before detailed discussions and DPAs for regulated industries.
Should we build this in-house instead?
If you have a senior ML team with spare capacity, sometimes yes. In practice in-house builds run 12–18 months because AI talent is hard to hire and the project competes with everyone's day job. This is our day job, so we compress that to weeks — and we'll set your in-house team up on the same tooling if you want to own it long-term.
Can the model integrate with our existing systems and stack?
Yes — we're stack-agnostic by design. We deploy to your cloud, on-prem, or edge and expose the model through versioned APIs and SDKs that plug into your apps, CRM, ERP, or BI dashboards. Integration and monitoring are part of the build, not a later phase.
Do you build models from scratch or fine-tune existing ones?
Both, depending on what the use case justifies. We fine-tune or apply RAG to open models like LLaMA, Mistral, and Gemma when that's faster and cheaper, and train from scratch on proprietary data when accuracy or licensing demands it. We work across PyTorch, TensorFlow, and scikit-learn — the choice follows the problem, not a house preference.
Can models run on-premise or in our private cloud?
Yes. We deploy private LLMs and ML models entirely within your environment — cloud, on-prem, or edge — with secure APIs, monitoring, and MLOps pipelines, so sensitive data never leaves your boundary.
What does a custom AI/ML model cost, and how long does it take?
Most engagements land between $50K and $250K depending on data readiness, model complexity, and integration scope, with first production models typically shipping in 8–16 weeks. If you're unsure where your use case fits, we start with a fixed-scope discovery sprint that returns a prioritized roadmap and ROI projection. Book a 45-minute scoping call and we'll give you a band on the first call.
