Artificial Intelligence

AI that does real work, with people in control.

We design, build and run production AI: assistants, agents and document intelligence wired into your workflows, grounded in your data and governed from day one.

Book a discovery call See client impact

Agent trace

    Automated today1,284Sent to people96Avg. handling38 s
    30+
    AI systems in production
    70%
    Typical tasks fully automated
    100%
    Decisions traceable to a source
    10wks
    Typical time to a live pilot

    The problems we solve

    If this sounds familiar, your AI is stuck in pilot.

    Most AI problems are not model problems. They are about fit, data, trust and ownership. Here is what we hear from engineering and product leaders, and what changes.

    “Our AI pilot never made it to production.” The demo worked. There were no evaluations, no integration and no owner.
    What changes

    Production engineering from day one: evaluations, integration, monitoring and a named owner.

    “We don’t know where AI actually fits.” Every team has ideas and nobody can rank them.
    What changes

    A fit-and-value assessment that ranks use cases, and says no where rules or people do better.

    “The answers sound right, but they aren’t.” Confident answers, no sources and no way to check them.
    What changes

    Retrieval grounded in your data, with citations, guardrails and accuracy measured every release.

    “Legal and security keep blocking it.” PII, data residency and unclear accountability.
    What changes

    Responsible-AI controls: PII masking, access policies, audit trails and human review where stakes are high.

    “Inference costs are growing faster than the value.” The biggest model for every call, and nobody watching spend.
    What changes

    Model routing, caching and right-sizing, with cost per task tracked like any other metric.

    What we offer

    Six capabilities, one production AI practice.

    Start where the value is clearest. Each capability shares the same evaluation, security and operating foundations.

    01

    Agentic workflows

    Agents complete multi-step work across systems, with tools, approvals and clear limits.

    You getAutomated workflows with human checkpoints
    • LangGraph
    • Semantic Kernel
    • MCP
    • Temporal
    02

    AI governance & LLMOps

    Evaluations, red-teaming, monitoring and responsible-AI controls keep systems safe as they change.

    You getEval suites, guardrails, audit trails and cost dashboards
    • Langfuse
    • Promptfoo
    • Guardrails
    • OpenTelemetry
    03

    Document intelligence

    Read, classify and extract from contracts, claims, invoices and forms, with confidence scores and review.

    You getStructured data from documents, ready to act on
    • Azure AI Document Intelligence
    • Textract
    • LLM extraction
    04

    GenAI assistants & copilots

    Assistants for customers and teams, grounded in your knowledge and embedded in your workflows.

    You getA production assistant with evaluations and analytics
    • OpenAI
    • Claude
    • Azure OpenAI
    • Gemini
    05

    Predictive ML & decisioning

    Forecasting, scoring and anomaly detection with explanations people can check.

    You getProduction models with reason codes and monitoring
    • scikit-learn
    • XGBoost
    • PyTorch
    • MLflow
    06

    Retrieval, knowledge & semantic layer (RAG)

    Search and answer over your own content, respect permissions and cite sources.

    You getA trusted knowledge and metrics layer
    • pgvector
    • Pinecone
    • Azure AI Search
    • dbt Semantic Layer
    • Cube

    How AI uses CLEAR

    Every AI engagement starts with C247 CLEAR.

    Explore C247 CLEAR
    1. C ClarifyThe real work AI should take on, with people in control
    2. L LocateThe knowledge, data and systems the AI needs to work
    3. E EvaluateAccuracy, guardrails and cost per task in production
    4. A ArchitectModels, agents, human review and integration into your stack
    5. R RoadmapOne workflow live in production, then scale with LLMOps

    How we deliver

    From first prompt to AI that earns trust.

    We prove value on one workflow with real users before scaling, and measure accuracy, safety and cost at every release.

    1. 01

      CLEAR discovery

      Rank use cases, map data and systems, set accuracy, safety and cost targets.

      2–4 weeks
    2. 02

      Proof of value

      An evaluation set and a working prototype on real examples.

      Accuracy baseline
    3. 03

      Pilot in the workflow

      Live with real users, guardrails and human review in place.

      Value proven
    4. 04

      Scale & integrate

      More workflows and users on the same foundations.

      Workflow by workflow
    5. 05

      Run & improve

      Quality, drift and cost monitored; feedback turned into improvements.

      Ongoing
    01

    The team that joins

    Senior people, accountable for the outcome

    • AI/ML lead accountable for the system
    • AI and full-stack engineers models, agents, product
    • Data engineer retrieval and pipelines
    • Delivery lead reports in your rituals
    02

    Quality & safety gates

    Checked on every release, not once

    • Evaluation suite run on every change
    • Red-teaming and prompt-injection tests
    • PII and access review including data residency
    • Cost per task checked before release
    03

    After launch

    Watched, improved and yours to run

    • Monitoring quality, drift and cost
    • Feedback loop from human reviewers
    • Runbooks and model cards in your repo
    • Hand-over or a managed run

    "Think first. Then build." is not a delay before delivery. It is how we make delivery more dependable.

    Technology and partners

    The right models for your use case and risk.

    We are model-agnostic and design for change: swap a model without rewriting the system. These are the platforms we deliver on most often, layer by layer.

    Cloud

    Build, deploy, and scale secure cloud infrastructure.

    • AWS
    • Terraform
    • Docker
    • Azure
    • GCP
    • Kubernetes

    Stack decision principle: We do not force a fixed toolset. We choose technology after CLEAR clarifies the data, workflow, security posture, integration reality, operating cost, and ownership model.

    Client impact

    Building things that will last.

    Featured work across verticals: proof that the method works beyond one flagship project.

    View all work
    Regional insurance carrier

    Settling simple claims the same day with document AI and human review

    • InsurTech
    • Document AI
    • Workflow automation
    Residential property manager

    Filling vacancies faster with tenant matching and digital leasing

    • PropTech
    • Matching engine
    • Digital leasing
    Online learning provider

    An adaptive learning platform that keeps every learner on track

    • EdTech
    • Adaptive content
    • Web & mobile
    Regional logistics operator

    Turning delivery data into on-time decisions across the fleet

    • Logistics
    • Data pipelines
    • Routing intelligence
    Precision components manufacturer

    Predicting machine failures before they stop the line

    • Manufacturing
    • IoT data
    • Predictive models
    Multi-specialty clinic network

    Cutting patient intake time in half with a clinically reviewed assistant

    • HealthTech
    • Conversational AI
    • Human-in-the-loop
    Digital lending platform

    Same-day lending decisions with explainable risk scoring

    • FinTech
    • Risk models
    • KYC automation
    Specialty retailer

    One AI-assisted platform for every customer conversation

    • Retail
    • Conversational AI
    • Platform engineering
    01 / 08

    Connect

    Clarity before code.

    We will help clarify the workflows, data, users, risks, and build path before your team commits engineering budget.

    • Workflows
    • Data
    • Users
    • Risks
    • Build path
    Book Discovery Call Submit RFP