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Scenario: tender research for an engineering firm
An illustrative scenario shows how an engineering firm can bring research, requirements review and competitive context into one workflow.
Auftrag One Team··6 min read
The starting situation: Expertise in abundance, acquisition time in short supply
Engineering consultancies are among sectors most dependent on public contracts. Planning services for roads, bridges, drainage systems, flood protection or building systems, much of this work is publicly tendered. Yet acquisition remains a bottleneck in many firms: the engineers best positioned to assess which tenders fit lack the time for research.
This illustrative scenario follows a fictional engineering firm working in infrastructure planning, traffic systems, drainage and structural engineering. It describes possible decisions, not a customer's experience. All figures are example assumptions.
The challenge
Research fell to a partner and a technical draftsperson who shared the task. Together they invested roughly 6 hours per week, yet remained dissatisfied with results.
The specific problems:
- Fragmented source landscape: Planning services are tendered at federal, state and municipal levels. The firm had to regularly monitor 8 different portals, plus individual municipal and district websites
- Complex scope descriptions: Infrastructure projects are often named by project designation, not service type. "Construction of new bypasses for B 123" is a relevant tender for a traffic planning firm, but no keyword hit for "traffic planning"
- High share of irrelevant hits: Searching for "engineering consultancy" or "planning services" returned hundreds of results from specializations the firm didn't serve, from electrical to landscape architecture
- Missing competitive intelligence: The firm had no systematic picture of who it competed against in procedures. Pricing and strategy were based on intuition rather than data
The result: a win rate of just 15%. Of 100 submitted bids, 85 led nowhere, a massive resource loss for a 15-person firm.
Illustrative team question: Which information do we need for a sound go/no-go decision?
The solution
The firm switched tender research to Auftrag One with three clear goals: less time, better match quality, and data-driven bid strategy.
Automated research with industry profile
The company profile was detailed: delivery phases per HOAI (1-9), specializations (traffic systems, structural engineering, drainage), preferred project sizes and regional focus. The AI search used this profile to evaluate all new tenders daily and rank by relevance.
The decisive advantage over keyword search: the AI recognized that "drainage master plan for municipality Musterbach" is relevant for the drainage specialization, without the words "engineering consultancy" or "planning services".
Structured requirements analysis
For each relevant tender, the platform provided an overview of key requirements:
- Required references (quantity, minimum volume, service scopes)
- Personnel requirements (qualifications, professional experience)
- Award criteria with weighting (price vs. concept vs. references)
- Deadlines and tender procedures
This pre-analysis enabled a sound go/no-go decision in minutes rather than half an hour.
Competitive analysis as strategic tool
The biggest change came from competitive analysis. For the first time, the firm could systematically assess:
- Which consultancies regularly win with which contracting authorities?
- Which project types are heavily contested, where is competition lighter?
- Which award criteria drive decisions in practice?
These insights fundamentally changed bid strategy.
Possible observations in the scenario
After six months the impact was measurable, after a year, it had transformed the firm sustainably.
Time savings: 300 hours per year
| Before | After | Change |
|---|---|---|
| 6 hours/week research | 1 hour/week review | -5 hours/week |
| ~312 hours/year | ~52 hours/year | -260 hours/year |
| Plus: requirements analysis ~2 hrs/week | Automated analysis ~20 min/week | -40+ hours/year |
Total savings over 300 hours per year, at 120€ per hour for an engineering firm, that's worth €36,000.
Win rate: From 15% to 28%
The clearest improvement appeared in win rates:
| Period | Bids | Wins | Rate |
|---|---|---|---|
| Previous year (manual) | 62 | 9 | 15% |
| First half (with Auftrag One) | 34 | 8 | 24% |
| Second half | 38 | 11 | 29% |
| Full year | 72 | 19 | 26% |
In the second half, the rate stabilized at 28-29%, nearly doubling from the previous year.
The key wasn't submitting more bids, but better bids on better-matched tenders. Total bids increased only slightly (62 to 72), yet hit rate improved substantially.
Better bid strategy through competitive data
Competitive analysis led to three concrete strategy shifts:
1. Focus on municipalities and districts. The data showed the firm had above-average chances with municipal contracting authorities, but lost regularly at federal and state level against larger consultancies. Bid activity shifted accordingly.
2. Concept quality over price competition. In procedures where concept weighted 50%+ of award criteria, the firm's win rate was 38%. In purely price-based procedures only 12%. The firm concentrated on high-concept-weighting procedures.
3. Targeted reference development. Analysis revealed which reference types particular contracting authorities valued most. The firm documented completed projects more systematically, with the metrics actually requested in tender procedures.
Lower effort per bid
Automated requirements analysis also reduced per-bid effort:
- Go/no-go decision in 5-10 minutes instead of 30-60 minutes
- Bid completeness increased, no more formal rejections for missing documentation
- Total acquisition effort reduced by estimated 30%
What other engineering firms can learn
Three insights from this case are transferable to most planning consultancies:
Research time is not the biggest problem. 6 hours per week sounds manageable. The real damage comes from missed tenders and poorly prioritized bids. Time savings are a welcome side effect, the true leverage is in hit quality.
Bid less, bid better. Most engineering firms submit too many bids on mismatched tenders. Data-driven pre-selection, combined with competitive intelligence, improves win rates more than simply increasing bid frequency.
Competitive data changes strategy. Without data, bid strategy is guesswork. With data, you spot patterns: where are realistic chances? Where aren't they? Which award criteria play to your strengths?
Conclusion
Tender research is business-critical for engineering consultancies, and one still handled manually, time-consuming and incompletely in many firms. Automation with AI-powered matching solves not just the time problem, but improves overall acquisition strategy.
Better match quality, structured requirements analysis and competitive context can provide a useful framework for a firm's own assessment. The outcome depends on its profile, tender documents and each procedure.
Want to test how many matching tenders the AI finds for your consultancy? Start your free 30-day test. Your profile is set up in minutes, and you'll see the most relevant tenders for your specializations immediately. All available packages are on the Pricing page. An overview of all features helps you assess which features deliver most value for your firm.
This scenario is fictional and for illustration only. Its figures, tables and statements are example assumptions, not customer outcomes or testimonials.