Nine ways we build your AI advantage.

What's included

• Benchmarking against real use cases
• Model comparison and selection
• Cost-accuracy trade-off analysis
• Evaluation framework design
• Performance regression testing

How we deliver it in your environment

1. 1

   Define success on your terms

   We workshop your use cases, constraints (latency, cost, compliance) and the exact metrics that define "good" for your business.

2. 2

   Build a golden dataset

   Using your real, anonymised data we assemble a representative evaluation set with expected outputs — the ground truth every model is scored against.

3. 3

   Run an automated benchmark harness

   We stand up an eval harness inside your environment and run candidate models side-by-side on accuracy, latency, cost and safety.

4. 4

   Deliver a scored recommendation

   You receive a ranked comparison with a clear model + configuration pick and a projected cost model — no vendor bias.

5. 5

   Hand over the harness

   The evaluation framework is installed in your infra so you can re-run it on every prompt, model or version change and catch regressions early.

What's included

• Custom RAG and GraphRAG implementations
• LLM application development
• Agent systems with tool integration
• Knowledge graph construction
• Production deployment and monitoring

How we deliver it in your environment

1. 1

   Architect around your stack

   We design the RAG / agent architecture against your data sources, security model and infrastructure — cloud, VPC or on-prem.

2. 2

   Build inside your codebase

   Our engineers work in your repositories, following your standards, review process and CI/CD — not a black box handed over at the end.

3. 3

   Integrate securely

   We connect your data stores, internal APIs and tools via function calling / MCP, all behind your own authentication and network boundaries.

4. 4

   Deploy to your environment

   Systems are containerised and shipped to your cloud account or on-prem servers with full observability — traces, evals and cost dashboards.

5. 5

   Monitor and iterate

   We wire in regression evals and production monitoring so quality is measured continuously, not assumed after launch.

What's included

• Pipeline architecture and ETL/ELT
• Real-time streaming and batch processing
• Data warehouse and lakehouse design
• Data quality and validation frameworks
• Cloud infrastructure on AWS, Azure, GCP

How we deliver it in your environment

1. 1

   Audit your data landscape

   We map your existing sources, warehouse, volumes and SLAs to understand where things break and where they need to scale.

2. 2

   Design the architecture

   We design the pipeline and lakehouse topology — ETL/ELT, streaming or batch — tuned for your cloud on AWS, Azure or GCP.

3. 3

   Ship pipelines as code

   Everything is built as version-controlled, testable code (dbt, Airflow, Spark) running inside your own cloud account.

4. 4

   Enforce quality at the gate

   We add validation, data contracts and alerting so bad data is caught before it ever reaches production or your models.

5. 5

   Hand over with IaC and runbooks

   You get Terraform, documentation and runbooks — and the option for us to keep operating the platform for you.

What's included

• Technical roadmap development
• AI readiness assessment
• Architecture review and stack selection
• Use case prioritization
• Team training and knowledge transfer

How we deliver it in your environment

1. 1

   Run technical workshops

   We sit with your engineering and business teams to understand goals, constraints and where AI can move the needle.

2. 2

   Assess readiness

   We audit your data, infrastructure and team skills against your objectives to find the real gaps and quick wins.

3. 3

   Deliver a prioritised roadmap

   You get a sequenced plan with architecture, recommended stack, effort estimates and expected ROI — ready to execute.

4. 4

   Review your architecture

   We perform architecture and code reviews of your existing or planned AI systems and flag risks before they cost you.

5. 5

   Enable your team

   We train your engineers and transfer knowledge so your team can own and extend the work without depending on us.

What's included

• Self-updating knowledge bases
• Custom ingestion pipelines
• Deterministic retrieval systems
• Human-in-the-loop verification
• Multi-source data unification

How we deliver it in your environment

1. 1

   Map your knowledge sources

   We inventory your documents, databases, tickets and tribal knowledge — everything the system needs to learn from.

2. 2

   Build incremental ingestion

   Custom pipelines continuously ingest and refresh content so the knowledge base stays current without manual effort.

3. 3

   Implement deterministic retrieval

   Retrieval is cited and reproducible — every answer traces back to its source, no black-box guessing.

4. 4

   Add human-in-the-loop review

   We build verification and provenance workflows so experts can approve, correct and trust what the system surfaces.

5. 5

   Deploy inside your walls

   The system runs in your environment and plugs into your agents, search and analyst tooling.

What's included

• Air-gapped infrastructure design
• Local model deployment (Ollama, vLLM)
• Data sovereignty compliance
• On-premise vector stores
• Security audit and hardening

How we deliver it in your environment

1. 1

   Design the air-gapped topology

   We size and architect the on-prem or air-gapped environment to your workloads and available GPU hardware.

2. 2

   Deploy models locally

   Models run in-house via Ollama / vLLM with on-premise vector stores — no request ever leaves your network.

3. 3

   Map compliance controls

   We align the deployment to your regulatory requirements — data residency, access controls and audit logging.

4. 4

   Harden the stack

   We run a security audit and lock down networking, secrets and access before anything goes live.

5. 5

   Operate behind your firewall

   You get runbooks and monitoring, with optional managed support delivered entirely inside your environment.

What's included

• Dedicated AI / ML engineers
• Data and MLOps specialists
• Flexible contract and project hiring
• Fully managed embedded teams
• Rapid onboarding and handover

How we deliver it in your environment

1. 1

   Scope the role

   A short call defines the skills, stack and timeline you need — no generic staffing pitch.

2. 2

   Shortlist vetted engineers

   We match pre-screened AI, data and MLOps engineers to your exact requirements.

3. 3

   Interview with your team

   You run a technical interview and pick who joins — the decision stays with you.

4. 4

   Onboard into your tooling

   Engineers embed into your repos, standards and workflow and start shipping fast.

5. 5

   Manage and hand over

   We handle contracts and management, with clean handover whenever you scale down.

What's included

• Adaptive learning engines
• AI tutoring and Q&A assistants
• Automated grading and assessment
• Content generation and curation
• Learning analytics and insights

How we deliver it in your environment

1. 1

   Discover learning goals

   We map your curriculum, learner profiles and the outcomes you want to improve.

2. 2

   Design the adaptive engine

   We build the personalization logic and content pipeline around your material.

3. 3

   Integrate with your LMS

   The platform connects to your existing LMS and learner data securely.

4. 4

   Pilot with a cohort

   We run a live pilot, measure outcomes and tune the experience.

5. 5

   Roll out with analytics

   You launch at scale with dashboards that track engagement and results.

What's included

• Computer vision and perception
• Robotics and motion control
• Edge and embedded deployment
• Sensor fusion and real-time inference
• Digital twin and simulation

How we deliver it in your environment

1. 1

   Assess the environment

   We study your operating conditions, hardware and safety requirements on-site.

2. 2

   Design perception & control

   We architect the vision, control and sensor-fusion stack and select hardware.

3. 3

   Build for the edge

   Models are optimised and deployed to edge / embedded devices for real-time inference.

4. 4

   Validate in simulation

   A digital twin lets us test behaviour safely before touching the real world.

5. 5

   Deploy and monitor on-site

   We integrate, field-test and monitor the system in your physical environment.