Use case framing and feasibility
We start by testing whether your data can actually support the prediction or automation you want. You get a clear answer on feasibility, expected accuracy, and cost before serious money is spent.
008AI ML Development Services
AI and ML development that ships to production
We design, build, and deploy machine learning systems that run inside real products: prediction models, LLM features, document pipelines, and the infrastructure that keeps them accurate after launch. For CTOs and product leaders who need working AI in production, not another proof of concept that dies in a notebook.
AI005
What we build
AI and ML capabilities we deliver
Every engagement covers the full stack, from framing the problem and preparing the data to serving the model behind an API your team can call.
Data pipelines and feature engineering
Most ML failures are data failures. We build the ingestion, cleaning, and feature pipelines that turn scattered databases, logs, and documents into training-ready inputs.
Custom model development
Classification, forecasting, recommendation, anomaly detection, and computer vision models built and evaluated against your baseline. We pick the simplest approach that hits the accuracy target, not the most fashionable one.
LLM and generative AI features
Retrieval-augmented chat, document summarization, extraction, and agent workflows built on GPT, Claude, or open models. We handle prompt design, grounding, evaluation, and guardrails so outputs stay reliable.
MLOps and deployment
Models served behind versioned APIs with CI/CD, monitoring, and rollback. We deploy to your cloud, containerized with Docker, so your team owns and operates the system.
Monitoring and retraining
Accuracy drifts as your data changes. We instrument every model with performance tracking and set up retraining pipelines that catch degradation before your users do.
How we work
From your data to a running model
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Discovery and data audit
We map the business problem to a measurable prediction task and audit the data you have against it. If the data cannot support the goal, we say so now and propose what to collect.
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Prototype and validate
A short, focused build to prove the model clears your accuracy bar on real data. You see measured results against an honest baseline before committing to a full build.
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Production engineering
We turn the validated model into a production system: hardened pipelines, a serving API, tests, monitoring, and integration with your existing product and infrastructure.
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Launch and iteration
We ship, watch live performance, and tune. Your engineers get documentation and handover sessions so the system is theirs to run and extend.
Why Webisoft
Why teams build their AI with us
We are a full-cycle software studio, so your model lands inside well-built software instead of sitting in an isolated science project.
Production first
We engineer for the day after launch: versioning, monitoring, retraining, and rollback are part of the build, not an afterthought.
Senior engineers end to end
The people who scope your project are the people who build it. No handoff from a sales engineer to a junior bench.
Honest feasibility calls
If a rules engine or a simpler heuristic beats a model for your case, we will tell you and build that instead. You pay for outcomes, not for ML theater.
Full product context
Because we also build backends, APIs, and UIs, we integrate the model into your product properly: latency budgets, error states, and user experience included.
FAQ
AI and ML development questions
(4)- Less than most teams assume. Pretrained and foundation models mean many use cases now work with modest labeled datasets, since the heavy lifting was done during the model's original training. A discovery phase can establish exactly where a dataset stands, and when data is thin, a collection plan can be designed so the model improves as usage grows rather than waiting for perfect data up front.
- It depends on the task, the privacy constraints, and the unit economics at the expected volume. A common pattern is to start with a hosted API to validate the feature quickly, then move to a fine-tuned or self-hosted model if cost, latency, or data control demands it. Custom models make sense when the task is narrow and high-volume, while hosted APIs win for broad language tasks and fast iteration.
- A feasibility prototype typically takes a few weeks, while a production system is usually a matter of months depending on data readiness and integration depth. The biggest schedule risks are data cleanup and connecting the model into existing workflows, not the modeling itself. A discovery phase that produces a concrete estimate for the specific case is the standard way to de-risk the timeline before committing to a full build.
- In a well-structured engagement, the client does. Ownership should cover training code, data pipelines, model weights, and infrastructure configuration, delivered into the client's repositories and cloud accounts with documentation. Contracts should state this explicitly, since vendor lock-in through retained model ownership or undocumented systems is a real risk worth checking before signing.
AI and ML Capabilities
Where We Add Value in AI and ML Development
We build AI systems that survive contact with production: models and LLM applications with evaluation, monitoring, and cost control designed in from the start. These are the capabilities that separate a demo from a dependable system.
LLM Application Engineering
We build products on models from Anthropic, OpenAI, and open weight alternatives, with structured outputs, tool use, and guardrails against prompt injection and data leakage. Model choice is a measured decision: we benchmark candidates on your actual tasks for quality, latency, and cost per request before committing. Fallbacks and provider abstraction keep you from being locked to one vendor's pricing.RAG and Knowledge Systems
Retrieval augmented generation lets an LLM answer from your documents instead of its training data, and the retrieval layer is where these projects succeed or fail. We invest in chunking strategy, hybrid search combining vectors with keyword matching, permission aware retrieval, and reranking, then measure answer quality against a curated test set. Citations back to source documents come standard, because unverifiable answers do not get trusted.Classical ML Where It Wins
Plenty of business problems, forecasting, churn prediction, pricing, anomaly detection, are solved better and cheaper by gradient boosting or regression than by an LLM. We build these with scikit learn, XGBoost, or PyTorch, with feature pipelines and retraining schedules, and we will tell you when a well tuned classical model beats the fashionable option at a fraction of the inference cost.Evaluation and Testing
Every AI system we ship comes with an evaluation harness: a test set built with your domain experts, metrics that reflect business outcomes rather than academic scores, and regression checks that run before any prompt or model change reaches production. This is what makes iteration safe. Without it, every improvement is a guess and every model upgrade is a gamble.MLOps and Cost Control
We deploy models and LLM pipelines with versioning, monitoring for drift and quality degradation, and structured logging of inputs and outputs for later analysis. Token spend is engineered deliberately: caching, prompt compression, and routing easy requests to cheaper models routinely cut inference bills substantially. You get a per feature cost dashboard, not a surprise invoice.Data Privacy and Governance
AI features touch sensitive data, so we design the boundary explicitly: what leaves your environment, what gets logged, retention, and redaction of personal information before it reaches a third party model. For regulated contexts we support private deployments through AWS Bedrock, Azure OpenAI, or self hosted open weight models. Decisions are documented so your compliance team can defend them.
How We Work
How an AI and ML Engagement Runs
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Use Case and Feasibility Sprint
We start with two to three weeks of focused validation: define the task precisely, audit the data you actually have, and build a rough prototype against real examples to see whether current techniques reach useful accuracy. The deliverable is an honest feasibility read with measured baseline quality, a cost projection, and a build plan. Sometimes the finding is that the data is not ready, and hearing that early is the cheapest outcome. - 2
Evaluation Set Before Model
Before serious build work, we sit with your domain experts to create the test set that defines success: real inputs, expected outputs, and edge cases that matter. This becomes the fixed yardstick for every prompt, retrieval, and model decision afterward. It is the single highest leverage artifact in the project, and it is why our iteration converges instead of wandering. - 3
Iterative Build Against the Yardstick
We build in short cycles, measuring each change against the evaluation set and shipping behind flags to a pilot group as soon as quality clears the bar you set. Human in the loop review is standard early on, with automation expanding as measured confidence grows. You see quality curves and cost per request at every checkpoint, so scaling up is a data decision. - 4
Production Hardening and Handover
Before full rollout we harden the system: rate limits, fallbacks for provider outages, monitoring for drift and cost anomalies, and runbooks for the failure modes AI systems actually have. Handover includes the evaluation harness and retraining or prompt update procedures, so your team can improve the system safely without us. Ongoing model operations support is available where teams prefer it.
FAQ
AI and ML Questions Buyers Actually Ask
(6)- Less of it goes to models than people expect. The bulk is engineering around the model: data preparation, retrieval quality, evaluation, integration into your workflows, and the interface where people use it. A focused LLM feature such as document Q and A or a support assistant is typically a two to four month build, custom model work with training runs is a longer commitment. Ongoing inference cost is the number to watch, it scales with usage, and we project it per feature during feasibility so there are no surprises at volume.
- Not perfectly, but the feasibility sprint will tell you where the gaps are, and data reality is the most common reason AI projects underdeliver. For RAG systems the documents mostly need to exist and be accessible, we handle extraction and structure. For predictive models, the requirement is stricter: you need enough historical examples of the outcome you want to predict. When the data is not ready, we say so and scope the data work first, because a model trained on a mess produces a confident mess.
- By engineering, not by hoping. Grounding answers in retrieved documents with citations sharply reduces fabrication, structured outputs and validation catch malformed responses, and confidence thresholds route uncertain cases to a human instead of guessing. The evaluation set measures the failure rate honestly, so you know the real number rather than an impression from demos. For high stakes decisions we design the system as a drafting or triage assistant with human sign off, because some error rates are acceptable in a suggestion and not in an action.
- Yes, with several levels depending on your requirements. Commercial APIs can be used under enterprise terms with no training on your data, or accessed through your own cloud tenancy via AWS Bedrock or Azure OpenAI so data stays inside your compliance boundary. For the strictest cases we deploy open weight models on your infrastructure. We add redaction of personal information before external calls, logging controls, and documented data flow diagrams your privacy and legal teams can review, and we build to GDPR and Quebec Law 25 obligations where they apply.
- Start with prompting and retrieval, because they solve most business use cases, cost far less, and adapt instantly when requirements change. Fine tuning earns its cost when you need consistent style or format at scale, lower latency from a smaller specialized model, or behavior that prompting cannot reach, and it requires curated training data plus ongoing maintenance as base models evolve. We benchmark the cheap approach first and only recommend fine tuning when the measured gap justifies it. Vendors that lead with fine tuning are often selling effort you do not need.
- We begin with a scoping call to pick the use case with the best ratio of business value to technical risk, then run the feasibility sprint, two to three weeks, which ends with a working rough prototype on your real data and a measured quality baseline. That prototype is the honest go or no go moment, and the findings are yours either way. Teams usually have a pilot group using a production quality version within two to three months of a go decision. The best preparation is picking one painful, frequent, well defined task rather than a broad AI strategy.
