model = AutoModel.from_pretrained('bert-base')embeddings = encoder(text).detach()loss = criterion(logits, targets)optimizer.zero_grad()loss.backward()optimizer.step()with torch.no_grad(): preds = model(x).argmax(dim=-1)rag_chain.invoke({ 'query': prompt })vector_store.similarity_search(q, k=4)tokens = tokenizer.encode(prompt)stream = client.chat.completions.create( model='gpt-4o-mini', messages=history, stream=True,)agent.run(task, tools=[search, sql])metrics.log({ 'precision': p, 'recall': r })if confidence > 0.92: route_to_human()kfp.compile(pipeline, package_path='./out')trainer.fit(model, datamodule)wandb.log({ 'val/loss': val_loss })schedule = CosineAnnealingLR(opt, T_max=50)dataset = load_dataset('imdb', split='train')deploy(model, env='prod', region='ap-south-1')model = AutoModel.from_pretrained('bert-base')embeddings = encoder(text).detach()loss = criterion(logits, targets)optimizer.zero_grad()loss.backward()optimizer.step()with torch.no_grad(): preds = model(x).argmax(dim=-1)rag_chain.invoke({ 'query': prompt })vector_store.similarity_search(q, k=4)tokens = tokenizer.encode(prompt)stream = client.chat.completions.create( model='gpt-4o-mini', messages=history, stream=True,)agent.run(task, tools=[search, sql])metrics.log({ 'precision': p, 'recall': r })if confidence > 0.92: route_to_human()kfp.compile(pipeline, package_path='./out')trainer.fit(model, datamodule)wandb.log({ 'val/loss': val_loss })schedule = CosineAnnealingLR(opt, T_max=50)dataset = load_dataset('imdb', split='train')deploy(model, env='prod', region='ap-south-1')

AI Services

AI and ML services built to ship

End-to-end AI development for startups, from proof of concept to production. Choose the capability your team needs, or let us design the right stack for your use case.

8 weeks to PoCProduction-ready modelsMLOps and monitoring

Capabilities

Pick the capability your team needs.

Six connected practices for designing, building, and shipping production AI — choose one, or let us design the right stack for your use case.

AI/ML Development

Generative AI Solutions

Computer Vision

AI Automation & Agents

AI Strategy & Consulting

Product & PoC Development

How the service catalog fits together

Four capability pillars under one delivery team

Every engagement leans on one or two of these. The lines between them blur intentionally — the AI work usually does not stand alone.

Pillar 0101 / 04

Generative AI and LLM systems

Retrieval, agents, copilots, and structured output pipelines for production workloads.

Pillar 0202 / 04

Machine learning

Classification, forecasting, ranking, and vision models.

Pillar 0303 / 04

Automation and agents

Workflow automation, tool-using agents, and human-in-the-loop systems.

Pillar 0404 / 04

Product and PoC engineering

Full-stack delivery — web, API, and infrastructure — around the AI core.

30+

AI services in catalog

Capability pillars

Industry verticals

2 wks

From scope to first sprint

Build protocol · 05 phases

A smooth path from AI ambition to production-grade system.

The experience should feel controlled from the first call — clear milestones, weekly demos, no mystery around what is happening.

Phase deckest. 6–12 wks total

Discovery

1 wk

Architecture

1–2 wks

Build

4–8 wks

Validate

1–2 wks

Ship

ongoing

phase A · 01◉
Discovery
Problem framing, data audit, success metrics, and risk surface — what the AI must never get wrong before we write any of it.
1 wk
phase B · 02◫
Architecture
Models, RAG, agents, APIs, evaluation, monitoring, and integration boundaries drawn against your real stack.
1–2 wks
phase C · 03λ
Build
Sprint-driven slices: clickable UX, backend orchestration, model behavior, and acceptance criteria visible weekly.
4–8 wks
phase D · 04Σ
Validate
Evaluation harnesses, shadow traffic, regression sets, and human review queues before any user-facing rollout.
1–2 wks
phase E · 05⌬
Ship
CI/CD, environments, observability, alerting, fallback paths, and an ownership doc that survives a vendor change.
ongoing

Field results

What founders and CTOs say after we ship together.

Across fintech, healthtech, legaltech, edtech, D2C, and more — one production handover at a time.

in production

Fraud rate cut 11×

SoftUs gave us a containerized model, a monitoring dashboard, and a retraining pipeline by sprint five. Fraud dropped from 3.4% to under 0.3% within the first month.

5 wksto prod

0.3%fraud rate

11×improvement

Arjun Mehta

CTO

Fintech Payments — India / US

in production

From 3 hours to 20 minutes

Analysts went from three hours per compliance question to under twenty minutes, with sourced references. Not a single hallucinated answer in over 400 real queries.

89%time saved

0hallucinations

400+live queries

Sarah Chen

Head of Legal Ops

LegalTech — Singapore

in production

MT5 live in six weeks

Three agencies failed on this. SoftUs scoped it in one call, had a backtest environment in week two, and we went live on MT5 in week six. They also flagged two critical flaws in our strategy logic.

6 wksto cohort

1 callto scope

2flaws caught

Marcus Forde

Co-Founder

Trading SaaS — UK

in production

60% contractor spend cut

SoftUs built a computer vision pipeline that handles 94% of cases automatically at 96% field-level accuracy. We cut contractor spend by more than half in 60 days.

0.96accuracy

60%cost cut

60 dto ROI

Priya Nair

Chief Product Officer

HealthTech — United States

in production

MLOps unblocked in 2 sprints

SoftUs diagnosed the issue in half a day and shipped a full MLOps setup — model registry, retraining triggers, A/B shadow deploys. What we had failed to solve for three months took them two sprints.

2sprints

0downtime

11 wksunblocked

Lena Fischer

Lead ML Engineer

B2B Analytics — Germany

in production

97% straight-through processing

Document intake automation went from 38% to 97% straight-through in eight weeks. The audit log and policy guardrails made our compliance review trivial.

97%auto-resolve

8 wksto ship

0compliance flags

Daniel Okafor

Head of AI

Insurance SaaS — UK / EU

Start with clarity

Have an AI idea, messy workflow, or product vision? Let's make it buildable.

Bring the problem. We'll help shape the product, define the architecture, and show the fastest path to a serious first version.

A practical first roadmap in the discovery call
Architecture, timeline, and delivery options in plain English
Security, scalability, and reliability discussed upfront

Discuss your project View capabilities

Model registry

softus-rag-v4.2

live

187ms

Latency

128k

Context

$0.004

Cost / req

Evaluation suite

Faithfulness94%

Answer relevance97%

Citation accuracy99%

Deploy pipeline

prod / canary 25% — healthy