RANKSAGA · AI SERVICES
Production AI for the systems your business actually runs on.
RankSaga designs, builds, and operates AI systems for regulated commercial enterprises. Banks, insurers, healthcare networks, energy and industrial operators. Forward-deployed engineers ship retrieval, generation, fine-tuning, agents, and the evaluation infrastructure that keeps them honest, inside the environments your auditors and operators already trust.
Most enterprise AI work fails for a reason that has nothing to do with the model. It fails because the data was wrong, the retrieval layer was wrong, the eval harness did not exist, or the system was never embedded in the workflow it was supposed to change. We work the layer below the model.
WHAT WE DO
Seven capability lines, one engineering team.
RankSaga's AI services exist for organisations that have already tried the easy path. The pilot worked, the demo was convincing, and then the system met production data, real users, real latency budgets, and real auditors. That is the gap we live in. We are an engineering team, not a consultancy that hands off a slide deck and a vendor recommendation.
Our work concentrates on the parts of an AI system that determine whether it ships at all: the retrieval layer that decides what the model sees, the embedding and fine-tuning work that decides how well it understands your domain, the agent and tool-use scaffolding that gives it leverage on real workflows, and the evaluation harness that tells you when something has quietly regressed in production.
We bring published research with us. Our work optimising embedding models on BEIR delivered up to 51 percent retrieval improvement across the benchmark, and the model is open-source on HuggingFace. The same engineers who shipped that research run the systems we deploy for customers, including the AI application currently in production for the Australian Armed Forces inside an air-gapped environment.
CAPABILITY LINES
Seven services, built to compose.
01 / Capability
Vector Database Management
Architecture, deployment, and tuning for production vector databases. Pinecone, Weaviate, Milvus, pgvector, Qdrant. Index design, sharding, hybrid search, and the ops posture to keep them fast under load.
02 / Capability
Embedding Model Optimisation
Fine-tune embedding models on your domain corpus. Multiple Negatives Ranking Loss, hard-negative mining, and the eval harness to prove the lift. Our published BEIR work delivered up to 51 percent retrieval improvement.
03 / Capability
Semantic Search & Retrieval
End-to-end retrieval systems: chunking strategy, dense and sparse retrieval, re-ranking, query understanding, and the relevance metrics that tell you when retrieval quality has drifted.
04 / Capability
Retrieval-Augmented Generation
Production RAG pipelines that survive audit. Source attribution, citation rendering, hallucination guards, structured response formats, and the eval framework to keep generation grounded in your data.
05 / Capability
Fine-Tuning & Distillation
Custom fine-tuning of foundation models for domain-specific tasks. Distillation to smaller, faster, cheaper variants for production inference. LoRA, QLoRA, and full-parameter approaches selected to fit your latency and cost budget.
06 / Capability
AI Evals & Observability
Evaluation harnesses, regression suites, prompt and model drift detection, and production monitoring of LLM outputs. The infrastructure that catches a quality regression before your customer does.
07 / Capability
Agentic Systems & Tool Use
Multi-step agents with tool and function calling, planning loops, and human-in-the-loop checkpoints. Designed for workflows where the cost of a wrong action is high and the audit trail is non-negotiable.
HOW WE OPERATE
Forward-deployed, end-to-end, embedded.
We do not throw a model over a fence. RankSaga engineers integrate with your team, work in your environment, and stay deployed alongside the system. The engagement model is consistent across every capability line, only the technical surface changes.
01 / Step
Embedded Discovery
We sit alongside the team that owns the data and the workflow. In the first weeks we map systems of record, the people who use them, the regulatory and latency constraints, and the failure modes that make this AI work hard.
02 / Step
Build in the Real Environment
We build inside your environment from week one. VPC, on-premise, or air-gapped. Hardening, observability, eval infrastructure, and the audit trail are designed in from day one rather than added in a hardening sprint after a pilot.
03 / Step
Operate Under Load
We stay deployed. RankSaga engineers run alongside operators, ship the next iteration on customer feedback, monitor for drift, and keep the system aligned to the workflow it lives inside, for as long as the work requires.
WHAT YOU GET
Working systems, not slide decks.
01 / Deliverable
A system in production, not a prototype
Every engagement targets a working system inside your real environment. Pilot work runs against production data and a representative slice of users, not a curated demo set.
02 / Deliverable
An evaluation harness you own
We leave behind the eval infrastructure that measures the system. Regression suites, drift detectors, golden-set scoring, and dashboards your team operates after we go.
03 / Deliverable
Documented architecture and runbooks
Architecture diagrams, decision records, deployment runbooks, and incident playbooks written for the team that will operate the system, not for an executive readout.
04 / Deliverable
Embedded knowledge transfer
Your engineers work alongside ours from week one. Pairing, code review, design sessions. By the end of the engagement, the system is operable by your team without us.
PROOF
Published research and live deployments.
RankSaga's optimised E5 embeddings delivered up to 51 percent retrieval improvement on BEIR benchmark datasets. The model is published on HuggingFace, the methodology is in our research record, and the same engineering team currently operates an AI application in production for the Australian Armed Forces inside an air-gapped environment.
RANKSAGA · BEIR BENCHMARK · ADF DEPLOYMENT · 2026
- ·Published BEIR results across Scifact, nfcorpus, scidocs, and quora datasets.
- ·Open-source RankSaga-Optimised-E5-v2 model on HuggingFace.
- ·Live air-gapped deployment for the Australian Armed Forces.
- ·Sovereign Microsoft Azure and on-premise environments supported.
EXPLORE
Pick the surface that maps to your problem.
Adjacent
Vector Database Management →
Architecture, deployment, and tuning for production vector databases.
Adjacent
Embedding Model Optimisation →
Fine-tune embeddings on your domain corpus. Measurable retrieval lift.
Adjacent
Semantic Search & Retrieval →
Chunking, dense and sparse retrieval, re-ranking, relevance evaluation.
Adjacent
Retrieval-Augmented Generation →
RAG pipelines that survive audit. Attribution, grounding, evaluation.
Adjacent
Fine-Tuning & Distillation →
Custom fine-tuning and distillation of foundation and embedding models.
Adjacent
AI Evals & Observability →
Evaluation harnesses, regression suites, drift detection, production monitoring.
Adjacent
Agentic Systems & Tool Use →
Multi-step agents with tool calling, planning, and human-in-the-loop.
QUESTIONS
What enterprise customers typically ask.
How is this different from hiring a generic ML consultancy?+
We are an engineering team that ships and operates production AI systems. The work concentrates on the layers below the model, retrieval, embeddings, evaluation, and on the integration with your environment. We do not produce strategy decks or vendor selection memos as deliverables. We produce working systems and the eval infrastructure to keep them honest.
Can you work inside our VPC, on-premise, or air-gapped environment?+
Yes. Our defence practice ships into air-gapped Australian Defence environments, and the same engineering team handles VPC and on-premise deployments for commercial customers. We design for the residency, identity, and observability constraints of the target environment from week one.
Do you bring your own models, or use the customer's?+
Both. We work with foundation models from OpenAI, Anthropic, Google, AWS Bedrock, and others. We deploy and harden open-weight models, including our own published embedding work, inside customer environments. Model choice is driven by the workload, the residency posture, and the cost and latency budget.
What does a typical engagement look like?+
A discovery sprint to map the environment, the data, and the workflow. A small forward-deployed team building inside production constraints from week one. Working software in user hands within weeks. Ongoing embedded support to operate, evaluate, and iterate, for as long as the work requires.
Do you publish your work?+
Yes. Our BEIR benchmarking research and the resulting RankSaga-Optimised-E5-v2 embedding model are published openly. Customer-specific work is governed by NDA, but the underlying methodology and tooling we use are shared in our research record and open-source repositories.
How do you measure success on an engagement?+
Against the metric the system is supposed to move. Retrieval recall, user task completion, time-to-resolution, audit-pass rate. We agree the metric and the eval harness in discovery, instrument it from week one, and measure against it for the duration of the engagement and beyond.
ENGAGE
Bring us in early. We are most useful when the problem is hard.
Our engagements start with a real conversation, not an RFP response. If you operate in a regulated commercial environment and have an AI problem worth solving, we want to hear about it.