AI/ML Development
End-to-end AI solutions built for scale — from data pipelines to production-ready models that deliver measurable ROI.
An honest read on the work
No marketing voice. A direct explanation of what the engagement actually covers and what it does not.
AI/ML Development at SoftUs covers the full lifecycle of building a model-backed product feature: framing the problem, assembling the data, picking the right architecture, training and evaluating against a held-out test set, and deploying behind an API your application can actually call. We do not chase research-grade benchmarks. We focus on the boring parts that decide whether a model survives contact with production — data drift, label quality, latency budgets, cost per inference, fallback behavior when the model is wrong, and an evaluation harness your team can keep running after we leave.
This service is the right fit when the problem has a clear business signal but the path to a working system is unclear. That includes predictive use cases (churn, demand, pricing, risk), classification and ranking (lead scoring, document routing, recommendation), forecasting, anomaly detection, and the long tail of decisions buried inside operational workflows. If you already know the architecture you want and just need execution, we can plug in. If you do not, we will run a one-week framing pass before any code is written so the engagement starts with an agreed success metric rather than a vibe.
What separates a SoftUs engagement is that we treat the model as one component of a larger system. A trained model is roughly a third of the work. The remaining two thirds — feature pipelines, retraining triggers, monitoring, rollback, the runbook your on-call engineer reads at 2am — is where most AI projects quietly fail. We ship the system, not the notebook. Every engagement ends with a working deployment, a versioned model card, an evaluation harness wired into CI, and a written handoff your team can extend without us in the room.
We work hands-on with your data and your cloud. We will not export your data to a third-party service we control. If you have a data team, we collaborate. If you do not, we will scope what is needed and bring it. Every engagement ships incrementally — the first usable artifact lands inside the first three weeks so you can review direction before the bulk of the build.
Four situations this service fits
If you recognize yourself in one of these, the engagement will move quickly. If not, we will tell you in week one.
Series A SaaS adding a first AI feature
You have product-market fit and want to ship an AI-backed feature that holds up under real load. We help you avoid the prototype-to-production cliff and ship something you can actually charge for.
Operations team automating a manual workflow
A team spends hours per day on a repetitive judgment task — triage, scoring, routing, review. We replace 70 to 90 percent of the manual load with a model and keep humans in the loop for the rest.
Data-rich product with no model yet
You have years of usage data, transactions, or logs and a clear hypothesis about what it could predict. We help you turn that data into a working model and a deployed feature inside two months.
Existing model that needs to scale to production
A data scientist on your team built something promising in a notebook, but it does not run reliably, cost-efficiently, or repeatably. We productionize it, wrap it in MLOps, and hand it back.
Five phases, end to end
The same shape every engagement runs in. Scoped weekly, demoed weekly, with a written deliverable at the end of every phase.
- Phase 01
Discovery & Scoping
1 weekWe map the business problem to a model problem, agree on the success metric, audit the data you already have, and pick the simplest architecture that can plausibly work. The output is a scoped plan with a target accuracy band and a fixed timeline.
- Problem framing document
- Data audit and gap list
- Target metric and acceptance criteria
- Architecture decision record
- Phase 02
Data & Architecture
1 to 2 weeksWe build the data pipeline — ingestion, cleaning, labeling strategy, feature store — and stand up the training and evaluation harness. Models are versioned from day one. Nothing trained outside the harness counts.
- Reproducible data pipeline
- Train, validation, and test splits
- Baseline model on held-out test set
- Experiment tracking workspace
- Phase 03
Build & Iterate
3 to 5 weeksWe iterate on architecture, features, and hyperparameters against the agreed metric. Each iteration runs through the same harness so progress is comparable across runs. You see a weekly demo with numbers, not screenshots.
- Tuned candidate models
- Comparative evaluation report
- Error analysis on failure modes
- Weekly progress demo
- Phase 04
Validate & Harden
1 to 2 weeksWe stress-test the chosen model on adversarial and edge-case inputs, measure latency and cost at expected load, and add guardrails, fallbacks, and confidence-thresholded routing. The model only ships if it passes the acceptance criteria.
- Load and latency benchmarks
- Bias and edge-case test suite
- Guardrails and fallback policy
- Model card with limitations
- Phase 05
Deploy & Handoff
1 weekWe deploy the inference API to your cloud, wire up monitoring and retraining triggers, and run a handoff session with your engineering team. You leave with a runbook, alerting, and a written plan for the next three months.
- Inference API in your cloud
- Monitoring dashboard with alerts
- CI/CD and retraining pipeline
- Engineering runbook and handoff
Tangible artifacts, not slide decks
At handoff, you receive a working system plus the documentation, dashboards, and runbooks needed to operate it without us.
The full AI/ML stack, end to end
From data ingestion to model training to vector retrieval to evaluation, we work across the tools production AI teams actually rely on. Reliable, well understood, and easy to hand off.
Languages
ML & Modeling
LLM & GenAI
Data & Vectors
Cloud & MLOps
Observability & Eval
Three ways to work with us
Pick the shape that matches your stage. We will tell you honestly if a different model would serve you better.
Fixed-scope PoC
A four-to-six week sprint that takes one hypothesis from data to a working model behind an API, with a clear go or no-go decision at the end.
Validating a single AI bet before committing to a full build, or unlocking an internal budget.
Embedded Pod
A two-to-three person SoftUs pod (ML engineer, data engineer, tech lead) embedded with your team for three to six months, running standups and reviews with you.
Companies with a real product team that need ML capacity without the long hiring cycle.
Full-build retainer
We own the end-to-end build, from scoping through productionization, with a fixed quarterly retainer and a defined roadmap reviewed every six weeks.
Teams without in-house ML who want a delivery partner accountable for outcomes, not hours.
What you will gain
Concrete outcomes from our engagement — measurable impact you can track from day one.
50% faster time-to-market for AI features
Reduced manual processes by up to 70%
Production-ready models in under 8 weeks
Who we build for
We work across industries where data, AI, and automation unlock real competitive advantage.
SaaS
AI features that improve retention and reduce churn
Fintech
Fraud detection, compliance automation, risk scoring
Healthtech
Image analysis, diagnosis assistance, automation
Edtech
Adaptive learning, smart assessments, content curation
Case studies
Examples of how we deliver under real constraints — timelines, data quality, and production requirements.
AI Lead Generation Platform
Businesses struggled to access verified, large-scale contact data for lead generation, relying on scattered sources that lacked integration and accuracy.
We developed a data aggregation platform that consolidates contact information from over 10 sources, including Apollo and Lusha, into a single, structured database. By enhancing data accuracy and implementing seamless API integrations, our platform improved outreach efficiency for businesses.
Voice-Enabled AI Chatbot
Traditional chatbots lacked the human touch — text-only interfaces created disconnect and limited engagement. Clients needed a more natural experience mirroring real conversations.
Created a real-time, voice-and-video-enabled chatbot system that listens, sees, and speaks — integrating LLMs with live video input, speech recognition, Azure-based TTS, and custom voice cloning.
The honest answers
Direct responses to what you would ask on a first scoping call. If your question is not here, send it on the contact form and we will answer in writing within a working day.
How long does a typical engagement take?
A focused PoC runs four to six weeks. A production build from scratch is usually eight to twelve weeks. Embedded pod engagements run on a quarterly cadence. We will not start without an agreed scope and a timeline you can plan around.
Who owns the IP and model weights?
You do. Code, model weights, training data derivatives, and documentation belong to you from day one. We sign IP assignment as part of the MSA. We retain only generic patterns and learnings, not your data or models.
Do you sign a DPA and are you SOC 2 friendly?
Yes on both. We sign DPAs as standard, follow least-privilege access to your systems, and have worked inside SOC 2 and HIPAA controlled environments. We can map our process to your controls before kickoff if needed.
Can you work with our existing data team, cloud, and vendor stack?
Yes. Most of our engagements run inside the client cloud account on their existing stack. We adapt to your tools rather than forcing ours. If you have a data team, we collaborate at the pipeline boundary so ownership stays clear.
What happens after go-live — do you provide support?
Every build ends with a written runbook and a handoff session. After that, you can take it fully in-house, keep us on a monthly retainer for model maintenance and retraining, or run the support yourself with us on standby for specific issues.
How do you price?
Fixed-scope PoCs are quoted as a flat number. Production builds are quoted by phase with milestones. Embedded pods are billed monthly per seat. We share the full quote before the SOW so there is no surprise on the invoice.
Do you work with regulated industries?
Yes. We have shipped models inside fintech, healthtech, and legaltech where audit trails, explainability, and data residency matter. We can deliver under HIPAA, PCI, and equivalent frameworks when scoped that way from the start.
Can you start from a vague problem or do we need a spec first?
You can come in with a vague problem. The first week is explicitly for framing — we turn "we want to use AI for X" into a measurable model problem with a target metric. If we cannot frame it into something testable in week one, we tell you and refund.
Adjacent work we do
Engagements that often run alongside this one.
Bring this work in-house, fast
A thirty-minute scope call gets you a written plan and a fixed quote. No slide decks, no follow-up cycle.
Have an AI idea, messy workflow, or product vision? Let's make it buildable.
Bring the problem. We'll help shape the product, define the architecture, and show the fastest path to a serious first version.
A practical first roadmap in the discovery call
Architecture, timeline, and delivery options in plain English
Security, scalability, and reliability discussed upfront
Model registry
softus-rag-v4.2
187ms
Latency
128k
Context
$0.004
Cost / req
Evaluation suite
Deploy pipeline
prod / canary 25% — healthy