Engineering AI beyond the demo
01 /
Domain-Specific Language Models
Continued pretraining, supervised fine-tuning, synthetic-data pipelines and domain adaptation for specialist technical or business environments.
Entry point
A focused technical assessment of data readiness, baselines and adaptation options.
Business problem
General models do not reliably understand the organisation’s terminology, tasks, constraints or definitions of correctness.
Common failure mode
Beginning an expensive training run before establishing meaningful baselines and domain-specific evaluation.
Engineering scope
- Model and architecture selection
- Data collection and cleaning
- Domain corpus analysis
- Tokenisation analysis
- Continued pretraining
- Supervised fine-tuning
- Parameter-efficient adaptation
- Synthetic-data generation
- Training infrastructure
- Inference planning
Typical deliverables
- Technical assessment
- Data-readiness report
- Model baseline
- Training pipeline
- Evaluation suite
- Adapted checkpoint
- Deployment recommendations
02 /
Evaluation and Reliability
Evaluation systems that measure task performance, correctness, failure modes and operational behaviour—not merely training loss.
Entry point
A short evaluation design sprint tied to a concrete decision or release.
Business problem
Teams ship models based on demos or loss curves without knowing when the system fails in production.
Common failure mode
Optimising for a proxy metric that does not reflect real task correctness.
Engineering scope
- Task definition and success criteria
- Benchmark design
- Golden sets and adversarial cases
- Automated scoring pipelines
- Human-in-the-loop review
- Regression gates
- Cost and latency measurement
Typical deliverables
- Evaluation plan
- Private test suite
- Scoring harness
- Error taxonomy
- Baseline report
- Regression checklist
03 /
Agentic Systems
Tool-using AI workflows that combine models, memory, retrieval, structured execution and human control.
Entry point
A scoped agent prototype around one high-value workflow with explicit control points.
Business problem
Teams need multi-step AI workflows that act on internal systems without becoming opaque or unsafe.
Common failure mode
Building autonomous loops without evaluation, approvals or recovery design.
Engineering scope
- Task decomposition
- Tool and API integration
- State and memory design
- Guardrails and approvals
- Failure recovery
- Logging and observability
- Human oversight points
Typical deliverables
- Workflow architecture
- Agent prototype or production path
- Tool contracts
- Approval policy
- Observability plan
- Operational runbook outline
04 /
Knowledge and Retrieval Systems
Retrieval systems for organisations that need grounded access to technical, operational or proprietary knowledge.
Entry point
A retrieval diagnostic on a representative subset of the knowledge base.
Business problem
Critical knowledge is trapped in documents and systems that general chat interfaces cannot retrieve reliably.
Common failure mode
Treating retrieval as a solved embedding problem without measuring answer grounding.
Engineering scope
- Corpus analysis
- Chunking and metadata design
- Embedding and index strategy
- Hybrid retrieval
- Reranking
- Citation and grounding
- Access-control modelling
- Evaluation of retrieval quality
Typical deliverables
- Knowledge-system architecture
- Ingestion pipeline
- Retrieval evaluation set
- Grounded answer interface design
- Access model recommendations
05 /
Machine Learning Infrastructure
Training, deployment and monitoring infrastructure designed around practical constraints.
Entry point
An infrastructure review tied to a concrete training or deployment bottleneck.
Business problem
Model work stalls because training, inference or monitoring infrastructure cannot support the required throughput, cost or reliability.
Common failure mode
Overbuilding platform abstractions before a single reliable training or serving path exists.
Engineering scope
- Training pipeline design
- Distributed training setup
- GPU memory and throughput optimisation
- Experiment tracking
- Serving architecture
- Monitoring and alerting
- Cost and capacity planning
Typical deliverables
- Infrastructure assessment
- Pipeline implementation
- Serving design
- Monitoring plan
- Cost-performance report
Strong engagement fit
- A company has valuable domain data but no adaptation strategy.
- An AI prototype performs well in demos but fails inconsistently.
- A team needs private evaluations for a specialist task.
- An agent must interact safely with internal systems.
- A training pipeline is constrained by GPU memory, cost or throughput.
- A product requires grounded access to a large knowledge base.
- An engineering team needs a senior AI specialist for a defined workstream.
When we may not fit
Asthra may not be the right fit when the primary need is bulk staff augmentation, generic chatbot reselling, high-volume annotation labour or a guaranteed research outcome.
- Bulk staff augmentation without technical ownership
- Generic chatbot reselling
- High-volume annotation labour as the primary need
- Guaranteed research outcomes or speculative product claims
Start with the problem, not the solution.
Discuss the technical contextNext step
Have a difficult AI problem?
Bring the domain, constraints and current system. We will help determine what is feasible, what should be measured and what is worth building.
Initial conversations are exploratory and confidential.