AI ENGINEERED FOR PRODUCTION · NOT BOLTED ON
Most B2B AI is a marketing layer on top of last decade’s product. We engineer AI into the data model from day one — embedded in real workflows, governed by the same compliance framework as the rest of the system, measured by impact on the operational metric that actually matters. We’re currently shipping 6 concrete AI capabilities into a HIPAA-architected healthcare SaaS for a US operator under NDA. Document intelligence, voice-to-text, risk detection, predictive scheduling, anomaly detection, and note summarisation — all engineered in, not retrofitted.
48-hour response · NDA on request · Your first call is with an engineer, not a sales rep
THE PROBLEM
The first wave of B2B AI from 2023–2025 followed a predictable pattern: take a 10-year-old SaaS, bolt an OpenAI API call onto a marketing-facing “chat” feature, raise the price 30%, ship a press release. Buyers learned to look past the badge. Most enterprise “AI” today is a feature flag on a system that has no underlying AI architecture — no vector store, no retrieval layer, no model-output validation, no compliance posture for the data flowing through the LLM.
It dies at data flow — PHI sent to a non-BAA-signed LLM endpoint. It dies at output reliability — a hallucinated medication on a caregiver note that the QA layer never caught. It dies at measurement — nobody actually tracks whether the AI improved the operational metric, so the feature ships, the price goes up, and the impact never gets validated.
Real production AI is different. It’s embedded in the data model. It runs with retrieval over your actual data, not just “ask GPT.” It has output validation layers, fallback paths, and observability for every inference. It treats compliance (HIPAA, GDPR, data residency) as a first-class constraint on which model to use and where it runs. That’s the AI we ship. The 6 AI capabilities we’re embedding into the Adult Home Care SaaS right now are all built to this standard.
OUR APPROACH
The first AI question isn’t “which model?” — it’s “does this use case need AI?” A lot of automation work is better done with deterministic rules, not LLMs. We separate the genuine AI use cases (variable input, fuzzy matching, language understanding) from the rules-engine use cases (deterministic logic, hard-coded workflows). Deliverable: a written use-case spec, recommended approach per use case, compliance/data-flow map, and a fixed-price quote.
Which model (managed API vs in-house fine-tune vs local), which runtime (cloud vs on-prem vs hybrid), which retrieval layer (vector DB vs straight search vs hybrid), which validation layer (rule-based vs LLM-as-judge vs human-in-loop), which compliance posture (BAA-signed providers vs sovereign deployment). Each decision documented, each trade-off explained. Deliverable: AI architecture diagram, model selection rationale, evaluation plan with target metrics.
Two-week sprints with measurable evaluation at the end of each sprint. AI features ship with eval suites — not just “does it work in demo” but “what’s the precision/recall on our test set?” and “what’s the cost-per-inference at expected load?” Output validation, fallback paths, and observability built in from the first feature.
AI doesn’t finish at launch — the model changes (OpenAI ships GPT-5, Anthropic ships Claude 5, the price model shifts), and your data shifts (edge cases your test set missed, drift over time). The retainer covers prompt evolution, model migration, eval-set expansion, and cost optimisation. Quarterly AI reviews are standard.
CAPABILITIES WE SHIP
Not demo features. These are the AI patterns we’re currently embedding into a HIPAA-architected healthcare SaaS — production-ready, evaluated, compliance-cleared.
What it does: Extracts structured data from insurance cards, prior authorizations, contracts, medical intake forms, ID documents. Eliminates the highest-error data-capture point in most business workflows.
Reference deployment: AI Home Care SaaS — in production-ready development. Reduces intake from hours to minutes.
What it does: Real-time transcription of voice input (caregiver notes, customer call analytics, internal meetings), grammar cleanup, automated summarisation, structured output for downstream systems.
Reference deployment: Caregiver visit-note workflow in the AI Home Care SaaS — field-grade reliability.
What it does: AI surfaces signals from operational data streams — clinical risk indicators in caregiver notes, EVV anomalies that suggest fraud, financial anomalies in transactions, behavioural deviations in workflow patterns — before they become incidents.
Reference deployment: Clinical risk-flag detection + EVV anomaly engine in the AI Home Care SaaS.
What it does: ML models for operational forecasting — optimal caregiver-client matching, demand forecasting, churn prediction, dynamic scheduling, capacity planning. Reduces coordinator workload while improving the operational outcome.
Reference deployment: Predictive caregiver-client matching in the AI Home Care SaaS.
What it does: Multi-step AI workflows where the model takes actions across systems — not just chats. Designed with human-in-loop checkpoints, action audit logs, and rollback paths for regulated environments. We pick agentic vs traditional automation per use case.
Frameworks: Built on Anthropic, OpenAI, or in-house orchestration depending on compliance and cost.
What it does: Vector-based semantic search over your own data (not just keyword), recommendation engines tuned to your domain, retrieval-augmented generation (RAG) where the LLM is grounded in your actual content — not its training data.
Tooling: pgvector, Pinecone, Weaviate, or self-hosted; embedding models picked per data domain.
CASE STUDY DEEP-DIVE
A US healthcare operator engaged us to build the next generation of adult home care SaaS. The competitive insight wasn’t the workflow modules — agencies already have those. The competitive insight was AI: legacy home care SaaS competitors typically retrofit AI as a marketing layer over a 10-year-old product. We’re engineering AI into the platform from the data model up — six concrete capabilities, each tied to a specific operational metric the agency cares about.
Document intelligence for insurance cards, prior auths, and medical intake docs — eliminates the highest-error data-capture point in agency workflows. Voice-to-text caregiver notes with grammar cleanup, so caregivers can document while still in the client’s home, not three hours later. Clinical risk-flag detection that surfaces fall risk, medication non-adherence patterns, and hospitalisation likelihood from caregiver notes before they become incidents. Predictive scheduling for caregiver-client matching based on geography, skill match, language preferences, and historical care quality. EVV anomaly detection for fraud (clock-ins that don’t match GPS, identical notes across clients). Note summarisation and quality scoring for RN review and caregiver coaching.
Each of these capabilities ships with an evaluation suite, observable inference logs, fallback paths when the model is unavailable, and human-in-loop validation for high-stakes outputs (clinical risk flags route to RN supervisors before becoming alerts). The compliance posture is HIPAA-aligned end-to-end: BAA-signed model providers where PHI flows through them, sovereign / in-house models where it doesn’t. This is what production AI looks like for a regulated industry. Not a chat box on a marketing page.
TECH STACK
HOW WE ENGAGE
1–2 weeks · fixed fee
Best when you have an idea but no clear AI use case. We separate the genuine AI use cases from the rules-engine use cases, map compliance/data-flow requirements, recommend models, and write the evaluation plan. Deliverable: written use-case spec, model recommendation, compliance map, and a fixed-price quote for the build.
3–6 months · fixed price
Best when you have a spec and need a fixed budget. We ship production-ready AI features with evaluation suites, observability, fallback paths, and human-in-loop validation. Two-week sprints with end-of-sprint eval reports. The engineer you meet in discovery is the engineer who ships your AI.
Monthly · time-and-materials
Best post-launch — AI doesn’t finish at launch. Prompt evolution, model migration (when OpenAI or Anthropic ships a new model), eval-set expansion, drift correction, cost optimisation. Quarterly AI reviews on impact metrics. The retainer is how we stay on the AI features alongside you.
Standard contracts: MSA + SOW on request before our first call. Mutual NDA before discovery. BAA available for healthcare engagements where AI touches PHI. Source code and prompts owned by you on final payment.
QUESTIONS
We pick per project. Managed LLMs (OpenAI, Anthropic, Google) for use cases where model quality matters more than data sovereignty — with BAA signed before PHI flows through them. In-house fine-tuned models when data sovereignty, cost-per-inference at scale, or latency requires it. Most projects end up hybrid: managed LLM for the hard-language work, in-house deterministic models for the cheap-and-fast work.
Only with your explicit decision and the right contracts in place. For healthcare clients, we sign BAAs with model providers before any PHI flows through their APIs. For sovereignty-sensitive clients we deploy in-house models on infrastructure you control. The data-flow map is delivered in the discovery phase so you know exactly where every byte of input and output goes.
By design from day one. For high-stakes outputs (clinical risk flags, financial decisions, legal text) we use retrieval-augmented generation grounded in your actual data, output validation layers, and human-in-loop confirmation before the output reaches the end user. For low-stakes outputs (search ranking suggestions, content tagging) the validation layer is lighter. We don’t ship an LLM feature without an explicit answer to “what happens when it’s wrong?”
Every AI feature ships with an evaluation suite: a test set of representative inputs with expected behaviour, precision/recall metrics, latency targets, cost-per-inference budgets. We run the eval before each release. We also track impact on the upstream operational metric — not just “the model is accurate” but “the feature reduced the operational metric we care about by X%.”
1–2 weeks use-case discovery, then 3–6 months for production-ready. Single AI feature with a clean use case and managed LLM: closer to 3 months. Multi-feature AI system with in-house models, compliance constraints, and full evaluation suites: closer to 6.
Pricing: fixed-fee use-case discovery, fixed-price build, time-and-materials retainer. AI projects also carry an ongoing infrastructure cost (inference, vector DB, observability) that we estimate during discovery. Cost-per-inference at expected load is on the architecture document.
Yes — that’s what the retainer is for. The model market changes every 6 months (new versions, deprecations, pricing changes). The retainer covers prompt evolution, model migration when providers ship new versions, eval-set expansion for drift correction, and cost optimisation as inference prices fall.
We’re listed on Capterra at 4.8★ across 25 verified client reviews — with verified reviewers across real estate, automotive, sports, non-profit, marketing, and auction industries. Read the reviews on Capterra → AI-specific reviews will accumulate as our healthcare engagement reaches production.
Same engineering team. Same evaluation discipline. Your AI features, with the compliance posture, output validation, and impact measurement built in from day one — not bolted on after the press release.
Inherited an AI feature that doesn’t actually work? +977 9851038796 — Roshan Subedi, Founder & MD · AI audits reviewed directly