AI Development
Hybrid Search & Reranking Services
We improve retrieval quality using hybrid search and reranking: higher recall, better relevance, fewer misses, and measurable tuning for RAG assistants and semantic search products.
Overview
What this service is
Hybrid retrieval combines keyword and vector search so you get both exact-match precision and semantic recall for messy real-world queries.
Reranking improves relevance by scoring candidate results more carefully, reducing wrong context that causes poor answers in RAG systems.
We tune retrieval using a query set and metrics, then harden latency and caching so quality gains don’t create performance regressions.
Benefits
What you get
Higher recall for long-tail queries
Find relevant context even when users don’t use the exact same wording as your documents.
Fewer hallucinations in RAG
Better context selection reduces wrong answers caused by irrelevant or missing sources.
Better ranking for mixed content
Hybrid retrieval handles structured docs, FAQs, and long-form PDFs with stronger relevance.
Measurable quality improvements
Tuning is validated against a dataset so changes are repeatable and trackable.
Latency-aware design
Caching and query optimization keep response time fast as traffic grows.
Features
What we deliver
Hybrid retrieval implementation
BM25 + vector search composition, weighting, and query expansion for better recall.
Reranking integration
Cross-encoder or LLM-based reranking with thresholds and explainable diagnostics.
Metadata filters
Filters for doc type, product version, tenant/team boundaries, and access control patterns.
Retrieval evaluation
Query sets and metrics for relevance and coverage, with regression checks over time.
Latency optimisation
Candidate limits, caching, and batching strategies to keep retrieval fast and cost-aware.
Debug tooling
Expose retrieved chunks and scores so teams can inspect why an answer happened.
Process
How we work
Baseline + dataset
We gather sample queries and define retrieval metrics for your success criteria.
Hybrid retrieval build
We implement hybrid retrieval and filters on your chosen search + vector stack.
Reranking + tuning
We integrate reranking and tune weights/thresholds against your dataset.
Latency hardening
We optimize and add caching so quality improvements don’t slow responses.
Tech Stack
Technologies we use
Core
Tools
Use Cases
Who this is for
Support knowledge search
Improve recall and relevance across product docs, FAQs, and troubleshooting guides.
Internal policy assistants
Rank the right policy excerpt first, with filters for department and document version.
Product documentation copilots
Retrieve the most relevant sections from long docs and reduce wrong-context answers.
Search across PDFs
Handle long, noisy PDFs with hybrid retrieval and reranking tuned for real queries.
Multi-tenant RAG systems
Prevent cross-tenant leakage using strict filters combined with relevance scoring.
FAQ
Frequently asked questions
Not always. For many domains, hybrid retrieval improves recall significantly, especially when users ask in varied language or include product codes and exact terms.
It depends on constraints. We can use smaller rerankers for speed, or higher-quality reranking where accuracy is more important than latency.
It often does, because better retrieval reduces wrong context. We also recommend evals and guardrails for end-to-end reliability.
Yes. We expose retrieved chunks and scores so teams can debug and tune retrieval behaviour.
Yes. We can improve retrieval on top of your current ingestion and vector DB setup with minimal disruption.
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Regional
Delivery considerations for your region
Data and risk discovery (Australia)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Australia)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Australia)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Australia)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want better retrieval without guesswork?
Share your queries and content—we’ll tune hybrid search and reranking with an eval set and measurable targets.
Eval-driven improvements.