SDLC Corp AI Products

AI products are ready to deploy software solutions that automate a specific business process instead of being built from scratch. Common examples include AI agents and chatbots, document and invoice processing, voice and call automation, demand and sales forecasting, recommendation engines, computer vision inspection, and knowledge assistants trained on your internal content. Each one targets a measurable outcome such as fewer manual hours, faster response times or lower error rates.

A ready made product ships with the workflow, interface and integrations already built, so you configure rather than construct. It is typically live in weeks at a fraction of the cost. Custom development is the right route when your process, data model or compliance rules have no off the shelf equivalent. Most engagements start with a configured product and add custom modules only where the business logic genuinely differs.

Use a language model when the work involves text, speech or documents, such as understanding requests, summarizing, classifying or generating content. Use traditional machine learning when the data is structured and the goal is prediction, such as forecasting, churn scoring, pricing or anomaly detection. Many production systems combine both, so the language model handles the unstructured input and the ML model makes the numeric decision.

Yes. Our AI products connect through REST APIs, webhooks and native connectors to systems including SAP, Oracle NetSuite, Odoo, Dynamics, Salesforce, HubSpot, Zoho, SQL databases and document stores. Where an API is unavailable, we use middleware or robotic process automation bridges. Integration scope is confirmed during a short technical discovery before development begins.

A short brief is enough to start. Tell us the process you want to automate, the systems it touches, roughly how much volume runs through it each month, who the users are, any compliance or data residency rules, and your target budget range and go live date. If you already have a formal requirements document, send it as is and we will respond against your own format and evaluation criteria.

We run a discovery call, review your process and data access, and return a written proposal within three to five working days. It sets out the recommended solution, assumptions, integration list, phased milestones, commercial model, ongoing running costs and named team. Discovery is free. If deeper technical assessment is needed, we quote it as a paid workshop that is credited back against the build.

Yes, and we usually recommend it. A pilot runs on one process, one team or one site for four to six weeks with success criteria agreed in writing before it starts. You review measured results against those criteria and decide whether to scale, adjust or stop. Work completed during the pilot carries into the full rollout rather than being rebuilt.

Deploying a configured AI product typically costs $5,000 to $40,000, while custom AI solutions generally range from $25,000 to $250,000 or more depending on data quality, integration count and compliance requirements. Ongoing costs such as model usage, hosting, monitoring and retraining should be budgeted separately as a monthly figure rather than assumed to be zero after launch.

A proof of concept takes two to four weeks, a configured product deployment four to eight weeks, and a custom AI solution three to six months from discovery to production. The longest variable is rarely the model. It is data access, security review and integration approvals, which is why we scope those in week one.

Pick one metric before deployment and baseline it, such as hours saved per week, tickets resolved without a human, cost per document processed, or leads contacted within an hour. We instrument that metric inside the product so the before and after figure is reported automatically. Deployments tied to a single defined use case consistently outperform broad, undefined AI rollouts.

Less than most teams expect. Language model products work from your existing documents, policies and past tickets with no training dataset at all, using retrieval over your content. Predictive models need historical records, usually 12 to 24 months of clean transactional data. We run a data readiness check first and tell you plainly if the data will not support the use case.

No. Your data is never used to train public models. We deploy on enterprise API tiers with data retention disabled, in your own cloud tenant, or fully on premise with open weight models where residency rules require it. Access controls, encryption in transit and at rest, audit logging and GDPR, HIPAA or ISO 27001 alignment are configured to your policy.

Accuracy is agreed as a target before build and measured against a labelled test set, not promised in the abstract. Answers are grounded in your own sources with citations, and confidence thresholds route low certainty cases to a human instead of guessing. Every decision is logged, so you can audit what the system did and why.

You own your data, your knowledge base and any model fine tuned on it. For custom builds, source code, prompts, configuration and documentation transfer to you in full on final payment, with ownership assigned in the contract from day one. For licensed products, the platform stays ours while every dataset, output and integration you create remains yours and is exportable.

Our deployments target the repetitive work around a role, such as data entry, triage, lookups and first line responses, rather than the role itself. In practice teams redeploy the recovered hours to higher value work. Adoption improves markedly when the product sits inside tools staff already use and when they are shown what it does and does not decide on its own.

Support after launch covers accuracy monitoring, prompt and model tuning, retraining as your data shifts, integration maintenance, usage and cost optimization, and version upgrades as underlying models change. This runs as a monthly retainer with a defined response SLA, or as scheduled improvement cycles agreed in advance. Handover includes admin training and documentation.