Sense7ai

From prototype to production.

Sense7ai engineers production-grade applied AI and machine-learning systems — classification, NLP, retrieval-augmented generation, recommendation, and signal detection. We work in the territory between research prototype and production deployment, where most teams stall. Every model we ship includes audit trails, drift monitoring, and validation evidence appropriate to the regulatory context.

If you are assessing whether an AI or ML capability is ready to move into your production environment, our problem-framing and data-assessment stages are the right starting point — before any modelling begins.

Audience

Who this is for.

Two buyer profiles — newer builders standing up AI capability for the first time, and legacy fitters adding ML to systems they already run. The engineering posture is the same for both.

Newer buildersTeams shipping AI/ML capability for the first time — production systems built from data assessment through deployment, with validation evidence and monitoring instrumented from day one.
Legacy fittersTeams adding ML to systems they already run — no rip-and-replace. We work against your existing data substrate (databases, data warehouses, message queues, or ERPs exposed via API). Substrate-specific integration depth is scoped in discovery, against the systems you actually run.
Less idealPure research projects · single-notebook experiments · ad-hoc one-off model builds without an intended production destination.
What we offer

From data to production — with the human in the loop.

Sense7ai's applied AI and ML practice covers the full path from data assessment through production deployment and monitoring. Every model we build treats human oversight as a design requirement — approval thresholds, output explanations, and escalation paths are defined before any model goes live.

DeliveredIn production for affiliated-group engagements01 / 07
Capability 01

Classification and prediction models.

Supervised models for scoring, severity assessment, risk classification, and ranking — built against a defined ground-truth benchmark and validated before deployment. Human-review thresholds are set per policy: the model recommends, the human decides at the consequential boundary.

Shipped in
  • Zitacandidate-job scoring and bias-aware ranking
  • Verixadeviation severity classification
Our approach

Five phases. Data before models.

01Phase 01

Problem Framing.

What decision is being supported? Who acts on the output? What is the cost of being wrong? This frames every downstream choice — model type, validation standard, human-approval threshold, and monitoring cadence.

Tech stack

What we work with.

Modelling: Python (PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM); Hugging Face Transformers. Retrieval: LangChain / LlamaIndex; vector stores: pgvector, Pinecone, Weaviate, Qdrant. Orchestration: Prefect, Airflow, Dagster. Experiment tracking: MLflow, Weights & Biases. Deployment: AWS SageMaker, Azure ML, GCP Vertex AI, on-premise / air-gapped for data-residency-constrained workloads. Observability and drift monitoring: Arize, WhyLabs, Evidently.

We work in your environment. The specific frameworks and tools for any engagement are agreed during discovery — reflecting the customer's existing environment, integration constraints, and data residency requirements. The list above is the range our engineering team works in; not every tool applies to every engagement.

Client examples

Shipped engagements.

All current production engagements are with affiliated companies within the Aeonn Ark Group portfolio. This is disclosed openly in diligence — see security.

● In production

Verixa

Pharmacovigilance and deviation classification

Signal detection and deviation severity classification for a regulated pharma platform. Every model output routes to a pharmacovigilance reviewer; no case action is taken without a qualified-person assessment. Designed to satisfy FDA 21 CFR Part 11 and EU Annex 11 requirements.

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● In production

ArkOS

Lead-generation forecasting and audience recommendations

Time-series forecasting and recommendation models for sales and marketing workflows inside the ArkOS agentic platform — surfaced as ranked recommendations for human review before any consequential action.

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● In production

Zita

Candidate matching and bias-aware ranking

Semantic candidate-job matching and bias-aware ranking in a production applicant tracking system. Bias assessments on protected attributes conducted during validation; model output includes the features driving each recommendation so the recruiter can interrogate the reasoning.

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Applied AI & ML — common questions.

How long does an applied-AI project typically take?
A first production deployment with Sense7ai typically takes 12–24 weeks: data assessment and problem framing (2–4 weeks), baseline modelling (2–4 weeks), production build and validation (6–12 weeks), deployment and monitoring stand-up (2–4 weeks). Pharma and financial-services projects may extend due to validation evidence requirements and regulatory review.
We have run AI or ML pilots internally — how do we know when we are ready to move to production?
That is exactly what our problem-framing and data-assessment stages are designed to determine. In two to four weeks we assess data quality and lineage, define the decision the model will support, set the human-approval threshold, identify regulatory sensitivity, and establish the validation standard the production system will need to meet. The output is a written brief — not a commitment to build. Many regulated-enterprise teams find that a structured assessment of their pilot is the right step before committing to a production engagement.
Do you build on closed-source models or open-source?
Both, depending on customer requirements and data residency constraints. For regulated workloads requiring on-premise or data-residency-controlled deployments, we work with open-source models (Llama, Mistral, BERT family) on the customer's chosen infrastructure. For general workloads, we select the best-available model for the task during the design phase.
How do you handle model drift in production?
Every production model ships with drift monitoring covering input distribution drift, prediction drift, and performance drift against ground truth. Alerts route to the on-call engineer for investigation. Retraining cadence is defined per model based on drift risk; typical cadences range from monthly to quarterly and are documented in the model's operating plan.
How do you address bias and fairness in ML models?
Bias on protected attributes appropriate to the use case (race, gender, age, geography) is assessed during validation as standard — disparate-impact analysis, equal-opportunity metrics, and counterfactual fairness checks. Residual bias and recommended mitigations are documented before go-live. The human reviewer is always in the loop for any consequential decision the model informs.
Can you support model risk management for financial services customers?
Yes. For engagements involving model-assisted decisions, our approach is informed by SR 11-7 model risk management principles — human-approval gates on consequential outputs and decision trails captured per your records-retention requirements. Model governance documentation is scoped during discovery to fit your regulatory obligations and internal framework; customer audit rights are in our MSA.
What validation evidence do you produce for regulated pharma models?
Validation evidence is sized to the model's risk tier per FDA Computer Software Assurance (CSA) guidance — higher-risk components receive more rigorous validation packages; lower-risk components receive appropriately scaled documentation. Scope is agreed during discovery alongside your validation team. Every model carries a documented retraining schedule and a kill-switch procedure.
What is Sense7ai's pricing and engagement model for AI/ML work?
Engagements are fixed-scope under a written master services agreement. Commercials vary by deployment footprint (cloud / on-premise / air-gapped), model complexity, the set of capabilities under contract, validation evidence requirements, and timeline. A scoping call typically yields a written proposal in 5–7 business days. Sense7ai does not run single-sprint POCs or pay-per-prediction arrangements — every engagement targets a production system with full monitoring and drift detection instrumented from day one.

Ready to build?

Tell us about the decision you need to support or the model you are moving toward production. We respond within one business day.

Schedule a Scoping CallCheck the diligence pack