Prescriptive Analytics
Prescriptive Analytics Services We Offer
A forecast tells you what’s coming. Prescriptive analytics tells you what to do before it arrives.
Decision Optimization Modeling
We build optimization models that recommend the best allocation of a limited resource — budget, inventory, staffing — against your specific business constraints.
Dynamic Pricing & Offer Recommendations
We build systems that recommend pricing or offers tailored to demand signals, competitive positioning, and margin targets in near real time.
Next-Best-Action Systems
We build models that recommend the next best action for a given customer or situation — which channel, message, or intervention is likely to work best.
Scenario Simulation & Trade-Off Analysis
We build simulation tools that let your team test "what if" scenarios and see the trade-offs of different decisions before committing to one.
How We Build a Prescriptive Analytics System
A recommendation nobody trusts enough to act on isn’t prescriptive analytics — it’s an unused feature. We build for adoption.
Decision & Constraint Mapping
We identify the specific decision the system needs to support, and map the real business constraints — budget, capacity, policy — that any recommendation has to respect.
Predictive Foundation
We build or incorporate the predictive models the recommendation engine depends on, since prescriptive recommendations are only as good as the forecasts underneath them.
Optimization Model Design
We design the optimization logic that turns predictions and constraints into a ranked, specific recommendation rather than a vague direction.
Trade-Off Validation
We stress-test recommendations against edge cases and alternative scenarios to confirm the logic holds up outside the conditions it was designed around.
Pilot with Human Review
We roll the system out with a human-in-the-loop review step, so your team can validate recommendations against real judgment before trusting it fully.
Deployment & Feedback Loop
We deploy the system into your workflow and build a feedback loop that captures outcomes, so the recommendation logic improves as more decisions are made.
Why Businesses Choose Absolute Web for Prescriptive Analytics
A black-box recommendation nobody trusts doesn’t get used. We build for that trust from the start.
Explainable Recommendations
We design systems that show the reasoning behind a recommendation, not just a number — so your team can trust it enough to actually act on it.
Built Around Real Constraints
We incorporate your actual business constraints into the optimization logic, so recommendations are things you can implement, not theoretical ideals.
Predictive Foundation Done Right
Because prescriptive systems depend on the forecasts underneath them, we hold the predictive layer to the same rigor as a standalone predictive analytics engagement.
Designed for Adoption, Not Just Accuracy
We build human-in-the-loop review into the rollout, so trust in the system builds gradually instead of being demanded on day one.
Technologies We Use
We leverage the cutting-edge of the AI technology stack to build robust agents:
Large Language Models (LLMs)
OpenAI
(GPT-4)
Anthropic
(Claude 3.5)
(Gemini)
Open-Source
(Llama 3)
Open-Source
(Mistral)
Frameworks & Orchestration
LangChain
LlamaIndex
AutoGPT
CrewAI
Programming Languages
Python
Node.js
TypeScript
Cloud & Infrastructure
AWS
Microsoft Azure
Google Cloud Platform
(GCP)
Pinecone
Weaviate
Milvus
Frequently Asked Questions
What is prescriptive analytics?
Prescriptive analytics uses predictive models combined with optimization techniques and business constraints to recommend specific actions, rather than just forecasting what’s likely to happen.
How is prescriptive analytics different from predictive analytics?
Predictive analytics forecasts an outcome; prescriptive analytics takes that forecast a step further and recommends what to do about it, factoring in constraints and trade-offs.
What business problems is prescriptive analytics good for?
Pricing optimization, inventory and resource allocation, next-best-action marketing, staffing optimization, and scenario planning, among other use cases.
Do we need a predictive model in place before building a prescriptive system?
Not necessarily in advance — we can build the predictive foundation as part of the engagement if one doesn’t already exist, since prescriptive recommendations depend on it.
Will the system just make decisions automatically, or recommend them to a person?
That depends on your risk tolerance — most engagements start with a human-in-the-loop review step, with automation introduced gradually as trust in the recommendations builds.
How do you make sure recommendations respect our real business constraints?
We map your actual constraints — budget, capacity, policy, regulatory limits — directly into the optimization model, so recommendations are implementable, not theoretical.
Can we understand why the system made a specific recommendation?
Yes — we prioritize explainable outputs that show the reasoning and trade-offs behind each recommendation, rather than an opaque score.
Is our data handled securely and compliantly across US, UK, and Canadian regulations?
We follow data handling practices aligned with regional requirements (e.g., UK GDPR, Canada’s PIPEDA) and recommend legal review for your specific compliance needs.
How long does a prescriptive analytics engagement take?
Most engagements take 10–16 weeks, depending on whether a predictive foundation already exists and the complexity of the constraints being modeled.
How do I get started?
Book a free consultation — we’ll review the decision you’re trying to support and scope the engagement before any full project begins.